Context-aware query derivation for IoT data streams with DIVIDE enabling privacy by design

1.1.Background

In the healthcare domain, many applications involve a large collection of Internet of Things (IoT) devices and sensors [40]. Many of those systems typically focus on the real-time monitoring of patients in hospitals, nursing homes, homecare or elsewhere. In such systems, patients and their environment are being equipped with different devices and sensors for following up on the patients’ conditions, diseases and treatments in a personalized, context-aware way. This is achieved by integrating the data collected by the IoT devices with existing domain knowledge and context information. As such, analyzing this combination of data sources jointly allows a system to extract meaningful insights and actuate on them [66].

Integrating and analyzing the IoT data with domain knowledge and context information in a real-time context is a challenging task. This is due to the typically high volume, variety and velocity of the different data sources [2]. To deal with these challenges, semantic IoT platforms can be deployed [11]. They generally contain stream processing components that integrate and analyze the different data sources by continuously evaluating semantic queries. To deploy this, Semantic Web technologies are typically employed: ontologies are designed to integrate and model the data from different heterogeneous sources and its relationships and properties in a common, machine-interpretable format, and existing stream reasoning techniques are used by the data stream processing components [30].

Currently, the configuration and management of queries that run on the stream processing components of a semantic IoT platform are manual tasks that require a lot of effort from the end user. In the typical IoT applications in healthcare, those queries should be context-aware: the context information determines which sensors and devices should be monitored by the query, for example to filter specific events to send to other components for further analysis. For example, a patient’s diagnosis in the Electronic Health Record (EHR) determines the monitoring tasks that should be performed in the patient’s hospital room, while the indoor location (room) of the patient in a homecare monitoring environment determines which in-home activities can be monitored. Changes in this context information regularly occur. For example, the profile information of patients in their EHR can be updated, or the in-home location of the patient can evolve over time. Hence, the management of the queries should be able to deal with such context changes. Currently, no semantic IoT platform component exists that allows to configure, derive and manage the platform’s queries in an automated, adaptive way. Therefore, platforms typically apply one of two existing approaches to achieve this.

The first approach to introduce context-awareness into semantic queries is by defining them in a generic fashion. A generic query uses generic ontology concepts in its definitions to perform multiple contextually relevant tasks. This way, semantic reasoners will reason in real-time on all available streaming, context and domain knowledge data to determine the contextually relevant sensors and devices to which the query is applicable. The advantage of this approach is that such queries are prepared to deal with contextual changes: due to their generic nature, they should not be updated often. However, the disadvantage of highly generic queries is the high computational complexity of the semantic reasoning during their evaluation. This is caused by complex ontologies in IoT domains such as healthcare that require expressive reasoning [16]. In healthcare applications that involve a large number of sensors, it is practically challenging to do this in real-time: queries take longer to evaluate, causing lower performance and difficulty to keep up with the required query execution frequencies. Typically, central components in an IoT platform have more resources and are therefore more likely to overcome this challenge. However, running all queries on central components would require all generated IoT streaming data to be sent over the network, causing the network to be highly congested all the time. In addition, the central server resources would be constantly in use, and local decision making would no longer be possible. Importantly, this would also imply no flexibility in preserving the patient’s privacy by keeping sensitive data locally. Looking at local and edge IoT devices to run those generic queries instead, resources are typically lower, making the performance challenges an even bigger issue of the generic query approach.

An alternative approach that can be adopted is installing multiple specific queries on the stream processing components that filter the contextually relevant sensors for one specific task. Evaluating such non-generic queries reduces the required semantic reasoning effort, solving the performance issues of the generic approach. However, this approach even further increases the required manual query configuration and management effort for the end user: whenever the context changes, the queries should be manually updated. This is infeasible to do in practice. As a consequence, current platforms do not apply this approach often and mostly work with generic queries instead.

Moreover, the definition of generic stream processing queries does not contain any means to make the window parameters of the query dependent on the application context and domain knowledge. Currently, an end user should configure these query parameters, and cannot let the system define them based on the data. This can be a problem in some specific monitoring cases. For example, the size of the data window on which a monitoring task such as in-home activity detection should be executed, may depend on the type of task, and therefore be defined in the domain knowledge. Another example is when the execution frequency of a monitoring task depends on certain contextual events happening in the patient’s environment.

In addition, preserving the privacy of the patients is of utmost importance in healthcare systems [1]. In IoT platforms, lots of the data generated by the IoT devices can contain privacy-sensitive information. Depending on where the data processing components are being hosted, this privacy-sensitive data may have to be sent over the IoT network, potentially exposing it to the outside world. Therefore, the IoT data is ideally processed close to where it is generated to reduce the amount of information sent over the network as much as possible. With regards to this, a semantic IoT platform should enable privacy by design [21]: it should allow an end user to build privacy by design into an application by precisely controlling which data is kept locally, and which data is sent over the network.

Finally, a semantic IoT platform component that would solve the aforementioned issues, should also be practically usable. Currently, existing semantic IoT healthcare platforms use semantic reasoners or stream reasoners that are configured with existing sets of generic semantic queries [66]. Defining such queries and ensuring their correctness is a delicate and time-consuming task. Hence, a new component should not introduce a completely different means of defining generic queries, but instead reduce the required changes to these definitions to a minimum. This implies that it should start from the generic definition of stream processing queries. Moreover, the other configuration tasks of the component should also be as minimal as possible to increase overall usability.

1.2.Research objectives and paper contribution

In summary, there is a need for a semantic IoT platform component that fulfills the different requirements tackled in the previous subsection, so that it can be applied in a healthcare data management system. Hence, we set the following research objectives for the design of such an additional semantic IoT platform component:

This paper presents DIVIDE, a semantic IoT platform component that we have designed to achieve the presented research objectives. DIVIDE automatically and adaptively derives and manages the contextually relevant specific queries for the platform’s stream processing components, by performing semantic reasoning with a generic query definition whenever contextual changes occur. As a result, the derived queries will efficiently monitor the relevant IoT sensors and devices in real-time, and still do not require any real-time reasoning during their evaluation.

The contribution of this paper is the methodological design and proof-of-concept of the DIVIDE component, fulfilling the requirements associated with the above research objectives. In the paper, DIVIDE is applied and evaluated on a realistic homecare monitoring use case, to demonstrate how it can be used in a practical IoT application context that works with privacy-sensitive information.

1.3.Paper organization

The remainder of this paper is structured as follows. Section 2 discusses some related work. In Section 3, the eHealth use case scenario is further explained, translated into the technical system set-up, and semantically described with an ontology. Section 4 presents a general overview of the DIVIDE system. Further functional and algorithmic details of DIVIDE are provided in Section 5 and Section 6 using the running use case example, while Section 7 zooms in on the technical implementation of DIVIDE. Section 8 describes the evaluation set-up with the different evaluation scenarios and hardware set-up. Results of the evaluations are presented in Section 9, and further discussed in Section 10. Finally, Section 11 concludes the main findings of the paper and highlights future work.

2.1.Semantic Web, stream processing and stream reasoning

Using Semantic Web technologies such as the Resource Description Framework (RDF) and the Web Ontology Language (OWL), heterogeneous data sources can be consolidated and semantically enriched into a machine-interpretable representation using ontologies [11]. An ontology is a model that semantically describes all domain-specific knowledge by defining domain concepts and their relations and attributes. Within RDF, an Internationalized Resource Identifier (IRI) is used to refer to every resource defined in an ontology [31]. Semantic reasoners can interpret semantic data to derive new knowledge based on the definitions in the ontologies. The complexity of the semantic reasoning depends on the expressivity of the underlying ontology [53]. Different ontology languages exist. They range from RDFS, which has the lowest expressivity, to OWL 2 DL, which has the highest expressivity.

RDFox [55] and VLog [72] are state-of-the-art OWL 2 RL reasoners. OWL 2 RL contains all constructs that can be evaluated by a rule engine. These constructs can be expressed by simple Datalog rules. By design, these engines are not able to handle streaming data. However, RDFox can also run on a Raspberry Pi, and any ARM-based IoT edge device in general. In addition, previous research has shown it can also successfully run on a smartphone [48].

Notation3 Logic (N₃) [14] is a rule-based logic that is often used to write down RDF. N₃ is a superset of RDF/Turtle [25], which implies that any valid RDF/Turtle definitions are valid N₃ as well.

Stream Reasoning (SR) [30] state-of-the-art contains three main approaches: Continuous Processing (CP) engines, Reasoning Over Time (ROT) frameworks and Reasoning About Time (RAT) frameworks. CP engines have continuous semantics, high throughput, and low latency but do not perform reasoning. ROT frameworks solve reasoning tasks continuously with high throughput and low latency, but do not consider time. RAT frameworks do consider time in the reasoning task, but may lack reactivity due to the high latency. These various approaches each investigate the trade-off between the expressiveness of reasoning and the efficiency of processing [30].

RDF Stream Processing (RSP) identifies a family of CP engines that solve information needs over heterogeneous streaming data, which is typical in IoT applications. It addresses data variety by adopting RDF streams as data model, and solves data velocity by extending SPARQL with the continuous semantics [65]. Different RSP engines exist, such as C-SPARQL [9], CQELS [46], Yasper [70] and RSP4J [69]. Queries can be registered to these engines that are used to continuously filter the defined data streams. A data window is placed on top of the data stream. Parameters of the window definition include the size of the data window that is added to the query’s data model, and the window’s sliding step which directly influences the query’s evaluation frequency.

RSP-QL [29] is a reference model that unifies the semantics of the existing RSP approaches. RSP has been extended to support ROT in various ways: (i) solutions incorporating efficient incremental maintenance of materializations of the windowed ontology streams [10,44,54,73], (ii) solutions for expressive Description Logics (DL) [47,68], and (iii) a solution for Answer Set Programming (ASP) [52]. More central to ROT is the logic-based framework for analyzing reasoning over streams (LARS) [13] that extends ASP for analytical reasoning over data streams. LASER [12] is a system, based on LARS, that employs a tractable fragment of LARS that ensures uniqueness of models. BigSR [59] employs Big Data technologies (e.g., Apache Spark and Flink) to evaluate the positive fragment of LARS. C-Sprite [17] focuses on efficient hierarchical reasoning to improve the throughput and application on edge devices by efficiently filtering out unnecessary data in the stream. A similar approach to filter out unnecessary streaming data in ASP exists, by investigating the dependency graph of the input data [57]. RDF Event Processing (RSEP) identifies a family of approaches that extend CP over RDF Streams with event pattern matching [4]. RSEP extends RSP with a reactive RAT formalism with limited expressiveness [49]. RSEP-QL [28] is an extension of RSP-QL that incorporates the language features from Complex Event Processing (CEP) [50]. StreamQR [20] rewrites continuous RSP queries to multiple parallel queries, allowing for the support of ontologies that are expressed in the ELHIO logic. The CityPulse project [58] presents the combination of RSP, CEP and expressive reasoning through ASP.

The most advanced attempts to develop expressive Stream Reasoning increased the reasoning expressiveness, but at the cost of limited efficiency. DyKnow [38] and ETALIS [5] combine RAT and ROT reasoning, but perform CP at an extremely slow speed. STARQL [56] is a first step in the right direction because it mixes RAT, and ROT reasoning utilizing a Virtual Knowledge Graph (VKG) approach [75] to obtain CP. Cascading Reasoning [64] was proposed to solve the problem of expressive reasoning over high-frequency streams by introducing a hierarchical approach consisting of multiple layers. Although several of the presented approaches adopt a hierarchical approach [5,52,56], only a recent attempt has laid the first fundamentals on realizing the full vision of cascading reasoning with Streaming MASSIF [18].

2.2.Semantic IoT platforms and privacy preservation

Today, different IoT platforms exist that extend big data platforms with IoT integrators [22,43]. FIWARE [24] is a platform that offers different APIs that can be used to deploy IoT applications. Sofia2 [61] is a semantic middleware platform that allows different systems and devices to become interoperable for smart IoT applications. SymbIoTe [62] goes a step further and abstracts existing IoT platforms by providing a virtual IoT environment provisioned over various cloud-based IoT platforms. The Agile [35] and BIG IoT [19] platforms focus on flexible IoT APIs and gateway architectures, such as VICINITY [23] and INTER-IoT [36] which also provide an interoperability platform. bIoTope [42] addresses the requirement for open platforms within IoT systems development.

Zooming in on IoT-based healthcare systems, a large number of solutions have risen in the last few years [15,40,41,51]. Jaiswal et al. surveyed 146 healthcare for IoT solutions in recent years, and classified them in five categories: sensor-based, resource-based, communication-based, application-based, and security-based approaches. They identified scalability and interoperability as two big challenges that are yet to be solved by many systems. Especially the latter is a challenge with the heterogeneity of data originating from different sources. This challenge can be solved with Semantic Web technologies.

Focusing on IoT healthcare systems that involve semantic technologies, multiple solutions already exist. For example, in the topic of homecare monitoring, Zgheib et al. [76] proposed a scalable semantic framework to monitor activities of daily living in elderly, to detect diseases and epidemics. The proposed framework is based on several semantic reasoning techniques that are distributed over a semantic middleware layer. It makes use of CEP to extract symptom indicators, which are fed to a SPARQL engine that detects individual diseases. C-SPARQL is then employed on a stream of diseases to detect possible epidemics. While this approach zooms in largely on scalability for this specific use case, it does not offer any flexibility in making the SPARQL and C-SPARQL queries context-aware in a fully automated and adaptive way.

Moreover, Jabbar et al. [39] and Ullah et al. [71] both presented an IoT-based Semantic Interoperability Model that provides interoperability among heterogeneous IoT devices in the healthcare domain. These models add semantic annotations to the IoT data, allowing SPARQL queries to easily extract concepts of interest. However, these illustrative SPARQL queries require manual configuration effort and are not automatically ensuring context-awareness in a dynamic environment. In addition, Ali et al. [3] present an ontology-aided recommendation system to efficiently monitor the patient’s physiology based on wearable sensor data while recommending specific, personalized diets. Similarly, Subramaniyaswamy et al. [67] present a personalized travel and food recommendation system based on real-time IoT data about the patient’s physical conditions and activities. Again, these systems only work with static SPARQL queries to evaluate their system, not achieving context-awareness in an adaptive, dynamic environment.

In summary, many of the presented platforms are adopting a wide range of existing Semantic Web technologies to deal with the challenges associated with real-time IoT applications in complex IoT domains such as healthcare. These platforms typically combine different technologies that involve both stream processing and semantic reasoning components. They all have in common that the queries for the stream processing components are not yet configured and managed in a fully automated, adaptive and context-aware way.

Privacy by design is an approach that states that privacy must be incorporated into networked data systems and technologies, by default [21,45,60]. It approaches privacy from the design-thinking perspective, stating that the data controller of a system must implement technical measures for data regulation by default, within the applicable context. Privacy by design is a broad concept that is more concretely defined through seven principles that can be applied to the design of a system. One of these principles is that the privacy-preserving capabilities should be embedded into the design and architecture of IT systems. Another principle focuses on the importance of keeping privacy user-centric, ensuring that the design always considers the needs and interests of the users. Other principles focus on visibility and transparency, privacy as the default setting, proactive instead of reactive measures, avoiding unnecessary privacy-related trade-offs, and end-to-end security through the lifecycle of the data. Privacy by design is a key principle of the General Data Protection Regulation (GDPR) of the European Union [45].

3.1.Use case description

The homecare monitoring use case scenario presented in this paper focuses on a rule-based service that recognizes the activities of elderly people in their homes.

Use case background More and more people live with chronic illnesses and are followed up at home by various healthcare actors such as their General Practitioner (GP), nursing organization, and volunteers. Patients in homecare are increasingly equipped with monitoring devices such as lifestyle monitoring devices, medical sensors, localization tags, etc. The shift to homecare makes it important to continuously assess whether an alarming situation occurs at the patient. If an alarm is generated, either automatically or initiated by the patient, a call operator at an alarm center should decide which intervention strategy is required. By reasoning on the measured parameters in combination with the medical domain knowledge, a system could help a human operator with choosing the most optimal intervention strategy.

A core building block of a homecare monitoring solution is an autonomous activity recognition (AR) service that detects and recognizes different in-home activities performed by the patient. Moreover, it should also monitor whether ongoing activities belong to a known regular routine of the patient, so that anomalies in the patient’s daily activity pattern can be detected. Such a service could make use of the data collected by the different sensors and devices installed in the patient’s home environment, as well as knowledge about AR rules and known routines of the patient. Given the heterogeneous nature of these different data sources, Semantic Web technologies are ideally suited to create this autonomous AR service.

Details of the activity recognition service The use case of routine and non-routine AR has been designed together with the home monitoring company Z-Plus. To properly perform knowledge-driven AR, AR rules should be known by the system. Z-Plus helped us with designing the rules.

An AR rule can be defined as a set of one or more value conditions defined on certain observable properties that are being analyzed for a certain entity type. An observable property is any property that can be measured by a sensor in the patient’s environment, e.g., temperature, relative humidity, power consumption, door status (open vs. closed), indoor location, etc. Every sensor analyzes its property for a specific entity. Examples of analyzed entities are a room (e.g., for a humidity sensor), an electrical appliance such as a cooking stove (e.g., for a power consumption sensor), a cupboard (e.g., for a door contact sensor), or even the patient (e.g., for a wearable sensor).

In a realistic home environment with a wide range of sensors installed, many different AR rules will be defined. This makes it highly inefficient to continuously monitor all possible activities that can be recognized in the home, since this would require the continuous monitoring of all sensors that observe a certain property for an entity type associated with at least one rule. Hence, the AR service performs location-dependent activity monitoring: it only observes activities that are relevant to the room that the patient is currently located in. To enable this, an indoor location system should be installed that unambiguously knows the current room of the patient at every point in time. The activities relevant to the current room can be derived by considering all sensors that analyze this room or an entity in the room: all activity rules should be evaluated that have conditions (i) on observable properties that are measured by these sensors, and (ii) that are defined for the same entity type as analyzed by those sensors.

Activities recognized by the AR service should be labeled as belonging to the regular routine of this patient or not. If an ongoing activity in the patient’s routine is recognized, the situation is normal and requires no more strict follow-up. Ideally, as long as an activity is going on, location changes in the home are less probable and should therefore be monitored less frequently. However, if an activity outside the routine of the patient is being detected, more strict location monitoring is required since the situation is abnormal. If necessary, an alarm should automatically be generated by the system. To implement such a system, knowledge on the existing routines of the patient at different times of the day should exist.

Finally, an important requirement of the AR service is that it reduces the information that leaves the patient’s home environment to a minimum, as a first step in preserving the patient’s privacy. This implies that no actual raw sensor data should be sent over the network. To enable this, the AR service should largely run in-home, so that only the actual outputs such as detected activities are being sent. Obviously, data that is not contained in the HomeLab should always be sent over a secure, encrypted connection.

Running example To facilitate the methodological description of DIVIDE in Sections 4, 5 and 6, consider the following illustrative running example derived from the presented homecare monitoring use case.

3.2.Activity recognition ontology

An Activity Recognition ontology has been designed to support the described use case scenario. This Activity Recognition ontology is linked to the DAHCC (Data Analytics for Healthcare and Connected Care) ontology [63], which is an in-house designed ontology that includes different modules connecting data analytics to healthcare knowledge. Specifically for the purpose of this semantic use case, it is extended with a module KBActivityRecognition supporting the knowledge-driven recognition of in-home activities.

The DAHCC ontology contains five main modules. The SensorsAndActuators and SensorsAndWearables modules describe the concepts that allow defining the observed properties, location, observations and/or actions of different sensors, wearables and actuators in a monitored environment such as a smart patient home. The MonitoredPerson and CareGiver modules contain concepts for the definition of a patient monitored inside a residence and the patient’s caregivers. The ActivityRecognition module allows describing the activities performed by a monitored person that are predicted by an AR model.

The DAHCC ontology bridges the concepts of multiple existing ontologies in the data analytics and healthcare domains. These ontologies include SAREF (the Smart Applications REFerence ontology) [26] and its extensions SAREF4EHAW (SAREF extended with concepts of the eHealth Ageing Well domain) [37], SAREF4BLDG (an extension for buildings and building spaces) and SAREF4WEAR (an extension for wearables), as well as the Execution–Executor–Procedure (EEP) ontology [33].

Listing 1 shows how a knowledge-based AR model can be defined and configured. In the example, it is configured according to the use case’s running example, i.e., with one activity rule for showering. Lines 13–17 of this listing contain the definition of the single condition of this rule.

In Section A.1 of Appendix A, additional listings detail multiple other definitions within the Activity Recognition ontology that support the knowledge-driven AR use case and its running example. This includes the ontological definitions that can be used by a semantic reasoner to define whether an activity prediction corresponds to a person’s routine, as well as the semantic description of the example patient and home in the running use case example.

Listing 1.

Example of how a knowledge-based AR model with an activity rule for showering can be described through triples in the KBActivityRecognition ontology module. All definitions are listed in RDF/Turtle syntax. To improve readability, the KBActivityRecognition: prefix is replaced by the : prefix.

3.3.Architectural use case set-up

To implement the use case scenario of a knowledge-driven routine and non-routine AR service, a cascading reasoning architecture is used [27]. An overview of the architectural cascading reasoning set-up for this use case is shown in Fig. 1. This architecture is generic and can be applied to different use case scenarios in the healthcare domain with similar requirements.

Fig. 1.

Architectural set-up of the eHealth use case scenario.

The architecture of the system is split up in a local and a central part. The local part consists of a set of components that are running on a local device in the patient’s environment. This device could be any existing gateway that is already installed in the patient’s home, such as the device for a deployed nurse call system. The local components are the Semantic Mapper and an RSP Engine. The components of the central part are deployed on a back-end server of an associated nursing home or hospital. They consist of a Central Reasoner, DIVIDE, and a Knowledge Base.

Knowledge Base The Knowledge Base contains the semantic representation of all domain knowledge and context data in the system, in an RDF-based knowledge graph. In the given use case scenario, this domain knowledge consists of the Activity Recognition ontology that is discussed in Section 3.2. It includes the AR model with its activity rules. The contextual information describes the different smart homes and their installed sensors, and patients.

Semantic Mapper The Semantic Mapper semantically annotates all raw observations generated by the sensors in the patient’s environment. These semantic sensor observations are forwarded to the data streams of the RSP Engine.

RSP Engine The RSP engine continuously evaluates the registered queries on the RDF data streams, to filter relevant events. In this use case scenario, the filtered events are in-home locations and recognized activities both in and not in the patient’s routine. Only these filtered events are encrypted and sent over the network to the Central Reasoner. By applying the cascading reasoning principles and installing the RSP Engine locally in the patient’s environment, a first step in preserving the patient’s privacy can be taken.

Central Reasoner The Central Reasoner is responsible for further processing the events received from the RSP Engine, and acting upon them. For example, it can aggregate the filtered events and save them to use for future call enrichment, or send an alarm to the patient’s caregivers when necessary. In general, any action is possible, depending on what additional components are deployed and implemented on the central node.

Importantly, the Central Reasoner will also update relevant contextual information in the Knowledge Base, such as events occurring in the patients’ environment. This information can then trigger a re-evaluation of the queries deployed on the local RSP engines. In the given use case scenario, relevant context changes that trigger a possible change in the deployed RSP queries are location updates and detected activities. When the in-home location of the patient changes, the set of activities that need to be monitored changes as well, since the AR service is location-dependent. Moreover, context information about recognized ongoing routine and non-routine activities directly defines the execution frequency of the location monitoring RSP query.

DIVIDE DIVIDE is the component that manages the queries executed by the local RSP Engine components. It updates the queries whenever triggered by context updates in the Knowledge Base. By aggregating contextual information with medical domain knowledge through semantic reasoning during the query derivation, the resulting RSP queries only involve filtering and do not require any more real-time reasoning. Moreover, it allows to dynamically manage the window parameters of the queries (i.e., the size of the data window and its sliding step) based on the current context. It is fully automated and adaptive, so that at all times, relevant queries are being executed given the context information about the patients in the Knowledge Base.

In the running example, DIVIDE will ensure that there is always a location monitoring query running on the RSP Engine component installed in the patient’s home. The window parameters of this query will depend on whether or not an activity is currently going on, and whether or not this activity belongs to the current patient’s routine. In addition, when the patient is located in the bathroom, an additional RSP query will be derived and installed that monitors when the patient is showering. When the query detects this activity, this would be considered a recognized routine activity as showering is included in the patient’s morning routine.

5.1.Initialization of the DIVIDE queries

A DIVIDE query is a generic definition of an RSP query that should perform a real-time processing task on the RDF data streams generated by the different local components in the system. The goal of DIVIDE is to instantiate this query in such a way that it can perform this task in a single query that simply filters the RDF data streams. To this end, the internal representation of a DIVIDE query contains a goal, a sensor query rule with a generic query pattern, and a context enrichment. These three items are essential for correctly deriving the relevant queries during the query derivation process. They will each be explained in detail in the first three subsections of this section.

5.1.1.Goal

The goal of a DIVIDE query defines the semantic output that should be filtered by the resulting RSP query. This required query output is translated to a valid N₃ rule. This rule is used in the DIVIDE query derivation to ensure that the resulting RSP query is filtering this required RSP query output.

For the generic query definition corresponding to the RSP query that detects the showering activity in the running example, the goal is specified in Listing 2. It is looking for any instance of a RoutineActivityPrediction.

Listing 2.

Goal of the generic DIVIDE query detecting an ongoing activity in a patient’s routine.

5.1.2.Sensor query rule with generic query pattern

The sensor query rule is the core of the DIVIDE query definition. It is a complex N₃ rule that defines the generic pattern of the RSP query, together with semantic information on when and how to instantiate it. Its usage by the semantic rule reasoner during the DIVIDE query generation defines whether or not this generic query should be instantiated given the involved context.

The formalism of the sensor query rule builds further on SENSdesc, which is the result of previous research [6]. This theoretical work was the first step in designing a format that describes an RSP query in a generic way that can be combined with formal reasoning to obtain the relevant queries that filter patterns of interest. By generalizing this format and integrating it into DIVIDE, it has become practically usable.

Each sensor query rule consists of three main parts: the relevant context in the rule’s antecedence, and the generic query and ontology consequences defined in the rule’s consequence.

Listing 3.

Sensor query rule of the generic DIVIDE query detecting an ongoing activity in a patient’s routine, for an activity rule type with a single condition on a certain property of which a value should cross a lower threshold.

Relevant context In the antecedence of the sensor query, the context in which the generic RSP query might become relevant is generically described. For each set of query variables for which the antecedence is valid, there is a chance that the rule, instantiated with these query variables, will appear in the proof constructed by the semantic reasoner during the query derivation. If this is the case, the query will be instantiated for this set of variables.

To explain the different parts, consider the DIVIDE query corresponding to the running example detecting the showering activity. Listing 3 defines the sensor query rule for the corresponding type of activity rule. The rule’s antecedence with the relevant context of the sensor query rule is described in lines 2–24. In short, it looks for AR rules relevant to the current room of the patient, following the definition of location-dependent activity monitoring in Section 3.1.

Generic query The generic query definition is contained inside the consequence of the sensor query rule. It consists of three main aspects: the generic query pattern, its input variables, and its static window parameters.

The generic query pattern is a string representation of the actual RSP-QL query that will be the result of the DIVIDE query derivation. This pattern is however still generic: some of its query variables still need to be substituted by actual values to obtain the correct and valid RSP-QL query. Similarly, the window parameters of the input stream windows of the RSP-QL query also need to be substituted.

The input variables that need to be substituted by the semantic reasoner in the generic query pattern are defined as a N₃ list. Every item in this list represents one input variable. This input variable is a list itself as well: the first item represents the string literal of the variable in the generic query pattern to be substituted, the second item is the query variable that should occur in the sensor query rule’s antecedence so that it is instantiated by the semantic reasoner if the rule is applied in the proof during the query derivation.

Similarly, the definition of the static window parameters is also a list of lists. Static window parameters are variables that should also be substituted by the semantic reasoner during the query derivation, but in the stream window definition instead of the query body or output. They are static as their value is directly defined by the value of the corresponding variable. Every item of the outer list is an inner list of three items. The first item represents the string literal of the variable in a window definition of the generic query pattern. The second item can either be a query variable or literal defining the value of the window parameter. If this is a query variable, it will be substituted during the rule evaluation based on the matching value in the rule’s antecedence, similarly to the input variables. The third item defines the unit of the value.

In Listing 3, the generic query definition is described in lines 28–33 and lines 41–69. More specifically, lines 28–29 and lines 41–69 define the generic query pattern, whereas lines 30–32 and line 33 define the input variables and static window parameters of the generic query, respectively.

Inspecting the example in Listing 3 in further detail, the generic RSP-QL query pattern string is defined in lines 44–59. The query filters observations on the defined stream data window :win of a certain sensor ?sensor with a value for the observed property ?prop_o that is higher than a certain threshold ?threshold (WHERE clause in lines 52–58). For every match of this pattern, output triples are constructed that represent an ongoing activity of type ?activityType in the routine of a patient ?patient, predicted by the activity recognition model ?model (CONSTRUCT clause in lines 44–49). These six variables are exactly the six input variables as defined in lines 30–32: their values will be instantiated during the query derivation. Note that the window parameter definitions specified in line 33 of Listing 3 define a window size of 30 seconds and a window sliding step of 10 seconds.

Ontology consequences The ontology consequences are the second main part of the sensor query rule’s consequence. This part describes the direct effect of a query result in a real-time reasoning context. This effect is obtained when a stream window of the generic RSP query would fulfill the pattern of the WHERE clause but no additional reasoning has been done (yet) to know the indirect consequences of this matching pattern. This is an essential aspect to understand: the purpose of DIVIDE is to derive queries that can make conclusions that are valid with the given context, through a single RSP-QL query without any reasoning involved. In a context without DIVIDE, these same indirect conclusions could only be made by performing an additional semantic reasoning step, based on the direct conclusions that are directly known from the matching query pattern. In other words, the triples defining the ontology consequences can be the same as the output of the generic RSP-QL query and thus the consequence of the rule representing the DIVIDE query’s goal. However, in practice, it will often require an additional semantic reasoning step to see whether the ontology consequences actually imply the output of the generic RSP-QL query.

In the running example, the direct consequences of a sensor observation matching the WHERE clause in lines 52–58 of Listing 3 would be the fact that an ongoing activity of the given type is detected for the given patient (lines 35–38). The indirect consequences represented by the definitions in the RSP-QL query output (lines 45–48) state that this is an activity in the patient’s routine.

5.2.Initialization of the DIVIDE ontology

To properly perform the query derivation, an ontology should be specified as input to DIVIDE by the end user. During initialization, this ontology will be loaded into the system. By definition, this ontology is considered not to change often during the system’s lifetime, in contrast with the context data. Therefore, the ontology should be preprocessed by the semantic reasoner wherever possible. This will speed up the actual query derivation process, since it avoids that the full ontology is loaded and processed every time the DIVIDE query derivation is triggered. To what extent the ontology can be preprocessed depends on the semantic reasoner used.

5.3.Initialization of the DIVIDE components

To properly initialize DIVIDE, it should have knowledge about the components it is responsible for. A component is defined as an entity in the IoT network on which a single RSP engine runs. For each DIVIDE component, the following information should be specified by an end user for the correct initialization of DIVIDE:

Upon initialization, all component information is processed and saved by DIVIDE. For every graph pattern associated with at least one component, DIVIDE should actively monitor for any updates to this ABox in the knowledge base, to trigger the query derivation for the relevant components when updates occur.

6.1.Context enrichment

Prior to actually deriving the RSP queries for the given DIVIDE query, the context data model can still be enriched. This is done by executing the ordered set of context-enriching queries corresponding to the DIVIDE query with a SPARQL query engine, if there are any, possibly after performing rule-based reasoning with the ontology axioms. The result of this step is a data model containing the original context triples and all triples in the output of any of the context-enriching queries, if there are any. Note that the output of the context-enriching queries can also contain dynamic window parameters to be used in the window parameter substitution step of the query derivation.

6.2.Semantic reasoning to derive queries

Starting from the enriched context data model, the semantic reasoner used within DIVIDE is run to perform the actual query derivation. This way, the reasoner will define whether the DIVIDE query should be initialized for the given context. If so, it specifies with what values the input variables and static window parameters, as defined in the query’s sensor query rule consequence, should be substituted in the generic query pattern of the DIVIDE query.

The inputs of the semantic reasoner in this step consist of the preprocessed ontology (i.e., all triples and rules extracted from its axioms), the enriched context triples, the sensor query rule and the goal of the DIVIDE query. Given these inputs, the reasoner performs semantic reasoning to construct and output a proof with all possible rule chains in which the goal of the DIVIDE query is the final rule applied. Every such rule chain will be (partially) different and correspond to a different set of instantiated query variables appearing in the goal’s rule.

To allow the semantic reasoner to construct a rule chain that starts from the context and ontology triples and ends with the goal rule, the sensor query rule is crucial. If the inputs allow the reasoner to derive the set of triples in the antecedence of the sensor query rule for a certain set of query variables, the rule can be evaluated for this set of variables. However, the semantic reasoner will only actually evaluate the rule for this set and include it in the rule chain, if the triples in the consequence of the sensor query rule (and more specifically, the part with the ontology consequences) allow the semantic reasoner to derive the antecedence of the goal rule. This can be either directly (i.e., without semantic reasoning) or indirectly (i.e., after rule-based semantic reasoning). If this is not the case, the sensor query rule will not help the semantic reasoner in constructing a rule chain where the goal is the last rule applied, for the given set of sensor query rule variables. Hence, if the proof contains an instantiation of the sensor query rule for a given set of query variables, this implies that the generic RSP-QL query of this DIVIDE query should be instantiated for this set. This should be done with those query variables of this set that are present in the list of input variables or window parameters of the sensor query rule’s consequence.

To reassure that this process works, consider the DIVIDE query parser’s translation of the ordered set of SPARQL queries in the end user DIVIDE query definition into its internal representation. If the original stream query in the SPARQL input would yield a query result, the final query’s WHERE clause might have a matching pattern, and thus an output. This is equivalent to the potential evaluation of the sensor query rule in the proof, depending on whether the sensor query rule’s consequence directly or indirectly leads to a matching antecedence of the goal rule.

Listing 4.

One step of the proof constructed by the semantic reasoner used in DIVIDE during the DIVIDE query derivation for the generic DIVIDE query of the running use case example. It shows how the sensor query rule in Listing 3 is instantiated in the proof’s rule chain. [...] is a placeholder for omitted parts that are not of interest.

6.3.Query extraction

The proof in the output of the semantic reasoning step can contain instantiations of the sensor query rule. If not, the proof will be empty, since this means that the semantic reasoner has not found any rule chain that leads to an instantiation of the goal rule. Every sensor query rule instantiation in the proof contains the list of input variables and window parameters that need to be substituted into the generic RSP-QL query of the considered DIVIDE query. In the query extraction step, DIVIDE will extract these definitions from every sensor query rule instantiation in the proof. Hence, the output of this step is a set of zero, one or more extracted queries.

The query extraction happens through two forward reasoning steps with the semantic reasoner used in DIVIDE. The outputs of both steps are combined to construct the output of the query extraction. The first reasoning step extracts the relevant content from the sensor query rule instantiations in the proof. For each instantiation, this content includes the instantiated input variables and window parameters, as well as a reference to the query pattern in which they need to be substituted. The second forward reasoning step of the query extraction retrieves any defined window parameters from the enriched context that are associated with the instantiated RSP-QL query pattern. Such window parameters may have been added to the enriched context during the context enrichment step. They will be used as dynamic window parameters during the window parameter substitution, while the window parameters occurring in the sensor query rule instantiations are considered as static window parameters.

Listing 5.

Output of the query extraction step of the DIVIDE query derivation, performed for the running example on the proof with a single sensor query rule instantiation presented in the proof step of Listing 4. The extraction of the dynamic window parameters (line 15) is done on the enriched context outputted by the context enrichment step.

6.4.Input variable substitution

In this step, DIVIDE substitutes the input variables of each query from the query extraction output into the associated RSP-QL query pattern. To achieve this, a collection of N₃ rules have been defined that allow to substitute the input variables into the query body in a deterministic way. Moreover, they ensure that the substitution is correct for IRIs and literals of any data type. To perform the substitution, the semantic reasoner used in DIVIDE performs a forward reasoning step. The input of this reasoning step consists of the substitution rules, the output of the query extraction step and the query pattern of the considered DIVIDE query. For each query in the query extraction output, the output of this step consists of a set of triples that define the partially substituted RSP-QL query body.

Listing 6.

Output of the input variable substitution step of the DIVIDE query derivation, performed for the running example on the query extraction output presented in Listing 5. The substitution is done using the generic RSP-QL query body of the corresponding DIVIDE query presented in Listing 3.

6.5.Window parameter substitution

In this step, the window parameters are also substituted in the partially instantiated queries to obtain the resulting RSP-QL query bodies. This is the final step that is performed by the semantic reasoner used in DIVIDE.

In general, DIVIDE offers the possibility to define the window parameters of derived RSP queries using semantic definitions. Currently, context-aware window parameters can be defined by an end user via the definition of a DIVIDE query. By separating the window parameter substitution from the other query derivation steps, DIVIDE offers the flexibility to trigger this substitution for other reasons than a context change. An example of this could be a device monitor observing that the resources of the device cannot handle the current query execution frequency.

Currently, to enable the substitution of use case dependent window parameters, DIVIDE makes the distinction between static and dynamic window parameters. For a static window parameter, the variable behaves as a regular input variable. This means that it should be defined in the consequence of a DIVIDE query’s sensor query rule with a triple similar to the following one:

This requires the variable ?var to occur in the sensor query rule’s antecedence. When defining a DIVIDE query as an end user using an ordered set of existing SPARQL queries, this can be achieved by ensuring that the variable name of this window parameter appears in the WHERE clause of the stream query, in a named graph that is not corresponding to a stream window. By definition, static window parameter variables will always receive a value in the query extraction output that can be used for substitution. In addition, dynamic window parameters are dynamically defined as triples in the outputs of context-enriching queries, similar to the following ones:

Importantly, dynamic window parameters will always overwrite static ones. This means that during the window parameter substitution, dynamic window parameters will be substituted first. Next, static window parameters are substituted for those window parameter variables in the RSP-QL query body that have not yet been substituted.

The substitution order of static and dynamic window parameters implies a few important things. Multiple dynamic window parameters can be defined in different context-enriching queries of the same DIVIDE query, to handle different situations. It is however the responsibility of the end user that no more than one definition occurs for each window parameter variable in the enriched context. If multiple values are defined for the same window parameter variable, the one that is substituted will be chosen arbitrarily. If no value is defined for a window parameter variable in the enriched context either, the value of the static window parameter with the same variable name will be substituted. However, if no static window parameter value is defined for this variable either, the default value in the end user definition of the DIVIDE query will be substituted. To make this work, DIVIDE will define a window parameter in the sensor query rule of the DIVIDE query with the given default value, for each such variable.

The actual substitution of window parameters is very similar to the input variable substitution. For both the static and dynamic window parameters, a forward reasoning step is performed with the semantic reasoner used in DIVIDE. The inputs of the reasoner are the output of the previous step and a collection of N₃ rules that ensure the correct substitution in a deterministic way. The unit of the window parameter, which is either a valid XML Schema duration string or a time unit, defines how the window parameter value is exactly substituted in the query body string.

6.6.RSP engine query update

The output of the window parameter substitution step is a set of instantiated, valid RSP-QL queries that are contextually relevant for the given component. These queries are however still presented as a series of semantic triples. This final step constructs the actual RSP-QL query string, translates the query to the correct query language and updates the registered queries at the component’s RSP engine.

Listing 7.

Final RSP-QL query that is the result of performing the window parameter substitution and query construction steps of the DIVIDE query derivation, performed for the running example on the input variable substitution output presented in Listing 6.

Query construction This step constructs an actual RSP-QL query from the set of prefixes and the instantiated query body triples in the output of the window parameter substitution step.

Query translation The definition of a DIVIDE component contains the query language of its RSP engine. If this language differs from RSP-QL, e.g., C-SPARQL, the RSP-QL query is translated in this step to this other language.

Query registration update The output of the previous step is a set of translated RSP queries for the given DIVIDE query. Since the DIVIDE query derivation is triggered because of a detected context change relevant to this component, the queries on the RSP engine of this component should be updated to reflect this new situation. To do so, DIVIDE keeps track of the queries that are currently registered on the RSP engine for the given DIVIDE query. In this step, DIVIDE semantically compares the new set of instantiated translated RSP queries with this existing set of registered queries. Based on this comparison, any registered queries that are no longer in the new set of contextually relevant RSP queries are unregistered. New queries that are not running yet on the RSP engine, are registered.

For completeness, it is important to mention that during the full DIVIDE query derivation, the query processing on the RSP engine of the corresponding component should ideally be temporarily paused. This is to avoid that incorrect filtering is done, since DIVIDE already knows that the active queries might no longer be contextually relevant as soon as DIVIDE is informed of a context change for this component. During the pause, incoming observations on the RSP engine’s streams should be buffered temporarily. This way, the queries can be restarted as soon as the RSP query update step finishes, and the buffered stream data can be fed to the RSP engine with their original timestamps.

7.1.Technologies

DIVIDE is implemented in Java as a set of Java JAR modules. These modules include the DIVIDE engine, which is the core of DIVIDE, the DIVIDE reasoning module and the DIVIDE server.

The DIVIDE reasoning module implements the ontology preprocessing and the query derivation steps with the semantic reasoner used in DIVIDE. Our implementation uses the EYE reasoner [74], which fulfills the requirements of the semantic reasoner explained in Section 4. This N₃ reasoner runs in a Prolog virtual machine.

The DIVIDE server module is an executable that starts up DIVIDE. It exposes a REST API that allows to add, delete and request information about DIVIDE queries and DIVIDE components in the DIVIDE system.

7.2.Configuration of DIVIDE

The configuration of DIVIDE is provided through a main JSON file. It includes details about different aspects of DIVIDE. In addition, the DIVIDE components can be defined in a separate file. An example of the JSON configuration of the DIVIDE system is provided in Appendix B.

Knowledge base The type of the knowledge base (e.g., Apache Jena, RDFox) should be configured, if it is deployed by the DIVIDE server. This is the preferred option when deploying new systems. If DIVIDE is deployed in an existing system, an existing Knowledge Base can also be used. In that case, the system will be responsible for monitoring context updates relevant to components registered to DIVIDE, and triggering the query derivation in the DIVIDE engine for those components whenever such a context update occurs.

Ontology To configure the ontology used by DIVIDE, the relevant ontology files should be specified.

Reasoner and engine The configuration of the DIVIDE reasoner and engine consists of a series of flags that allow to change the default DIVIDE behavior. For example, DIVIDE can be configured to handle TBox definitions in context graphs during the query derivation. Moreover, the parser can be configured to automatically create a variable mapping between the stream and final query based on equal variable names.

DIVIDE queries For every DIVIDE query, a separate JSON file should be linked in the configuration. This file can include the items of the internal representation of a DIVIDE query, or the end user definition of a DIVIDE query. In the latter case, the implementation of the DIVIDE query parser ensures that the parsed DIVIDE queries result in valid RSP-QL queries after the query derivation. This is achieved by validating the inputs, renaming the query variables to avoid any mismatches, ordering the input variables and static window parameters to obtain a deterministic substitution, and handling query variables in special constructs such as GROUP BY clauses.

The JSON configuration of the DIVIDE query for the running use case example can be found in Appendix B.

Server For the DIVIDE server, the host and port of the exposed REST API is defined. If DIVIDE deploys the Knowledge Base as well, the port of the Knowledge Base REST API available for context updates is also specified.

DIVIDE components The components known by DIVIDE should be defined in an additional CSV file, which contains one entry per component. The properties of every component entry are separated by a semicolon.

7.3.Implementation of the ontology preprocessing

During the initialization of DIVIDE, the configured ontology is preprocessed with the EYE reasoner in three steps. First, an N₃ copy of the full ontology is created. Second, specialized ontology-specific rules are created from the original rules taken from the OWL 2 RL profile description [53]. Starting the EYE reasoning process from these rules will reduce the computational complexity of the reasoning [7]. Third, an image of the EYE reasoner, which has already loaded the ontology and specialized rules, is compiled within Prolog. This precompiled Prolog image is the result of the ontology preprocessing. By starting the semantic reasoning step of the query derivation process from this image, the triples and rules do not need to be loaded into the EYE reasoner each time it is called during the DIVIDE query derivation. This allows to make the semantic reasoning step significantly more efficient.

Although considered infrequent, ontology changes can be handled by DIVIDE. If DIVIDE is hosting the Knowledge Base, ontology changes can be made by using the Knowledge Base REST API. Any TBox change will result in DIVIDE reloading the ontology, redoing the ontology preprocessing, and triggering the query derivation for all DIVIDE queries and components. This is a computationally intensive operation.

7.4.Implementation of the DIVIDE query derivation

The DIVIDE query derivation is managed by the DIVIDE engine. To decouple the scheduler of query derivation tasks from their actual parallel execution, the DIVIDE engine manages a blocking task queue and a dedicated processing thread for every DIVIDE component in the system.

Different tasks can be scheduled by the DIVIDE engine in the blocking task queue of a DIVIDE component. The main task type is a query derivation for one or all DIVIDE queries. In case of a context change, the query derivation is scheduled for all DIVIDE queries. However, when a new DIVIDE query is added to the engine via the server API, the query derivation is only scheduled for the new DIVIDE query. In case the query execution should be performed for multiple DIVIDE queries, the query derivation steps are executed in parallel threads for every DIVIDE query. Another task type is the removal of a DIVIDE query from a component, which requires all related RSP queries to be unregistered from the component’s RSP engine. This task is scheduled for all DIVIDE queries of a component when that component is removed, or for all components and one DIVIDE query when this DIVIDE query is deleted.

The following paragraphs present some further implementation details of some DIVIDE query derivation steps.

Context enrichment This step involves the execution of SPARQL queries prior to the actual query derivation. Hence, this is the only semantic step of the query derivation that is not necessarily performed by the EYE reasoner. This is the case if the queries contain SPARQL constructs that cannot be translated to a valid N₃ rule. In this case, the queries are executed in Java by using Apache Jena. In the other case, the queries are translated to N₃ rules which are then applied on the set consisting of triples and, if reasoning is enabled, also consisting of the ontology rules.

RSP engine query update This final step of the query derivation is not performed with the EYE reasoner. To update the query registrations at the RSP engines, the REST APIs of the RSP engine servers are used.

8.1.Evaluation scenarios

All evaluations are performed on the eHealth use case described in Section 3.1. This section zooms in on the details of the evaluation scenarios of this use case.

8.2.Performance evaluation of DIVIDE

To derive the contextually relevant RSP queries, DIVIDE performs multiple steps, both during initialization and the query derivation. To evaluate the performance of DIVIDE, the duration of the main steps is measured for the given evaluation scenarios. In concrete, the duration of the following steps is measured:

The duration of these steps is measured from within the execution of the DIVIDE server Java JAR, which is configured with the scenario’s ontology and DIVIDE queries to allow performing evaluation 1 and 2. To perform evaluation 3, the full context of the scenario is sent as new context to the DIVIDE Knowledge Base server.

While evaluating the performance of the DIVIDE query parser, the correctness of the parsing is also validated.

Technical specifications The evaluation is performed on a device with a 2800 MHz quad-core Intel Core i5-7440HQ CPU and 16 GB DDR4-2400 RAM.

8.3.Real-time evaluation of derived DIVIDE queries

The task of DIVIDE is to manage the RSP queries on the registered RSP engines. These RSP queries are characterized by the fact that they do not require any more reasoning during their continuous evaluation, since semantic reasoning during the query derivation ensures that they are contextually relevant at every point in time. This section compares the real-time performance of evaluating these derived RSP queries on the C-SPARQL RSP engine [9].

9.1.Performance evaluation of DIVIDE

Figure 3 shows the distribution of the duration of two initialization steps of the DIVIDE system: the preprocessing of the Activity Recognition ontology, and the parsing of the toileting query specified as SPARQL input. The preprocessing of the ontology on average takes 9,640 ms, with a standard deviation (SD) of only 42 ms. The average duration of the query parsing is only 64.87 ms (SD 2.76 ms). It was also validated that the parsing of the end user definition of the DIVIDE query to its internal representation was done correctly.

Figure 4 shows the performance results of the query derivation with DIVIDE, for the DIVIDE query corresponding to the toileting activity rule (also corresponds to the showering rule) and the DIVIDE query corresponding to the brushing teeth activity rule. Figure 4(a) shows the distribution of the duration of the query derivation for each individual query. The average durations of the query derivation are 3,578 ms (SD 38 ms) and 2,968 ms (SD 37 ms) for the toileting and brushing teeth DIVIDE queries, respectively.

Figure 4(b) shows the percentage of time taken up by the different substeps, averaged over all runs for the three DIVIDE queries. These substeps include all steps performed with the EYE reasoner: the reasoning (47.27% on average), the query extraction (27.32% on average), the input variable substitution (9.44% on average) and the window parameter substitution (10.93% on average). The remaining time (5.04% on average) is overhead of the DIVIDE implementation, including internal threading and memory operations.

9.2.Evaluation of DIVIDE in comparison with real-time reasoning approaches

Figure 5 shows the results of the comparison of the real-time evaluation with DIVIDE on a C-SPARQL engine with different real-time reasoning approaches, for the toileting query. The results show the evolution over time of the total execution time from the event generation until the routine activity prediction is generated by the engine. The measurements included in the graphs are averaged over the evaluation runs. For three setups, there are no measurements shown for the full time course of the evaluation, which takes 30 minutes. These set-ups are the pipe of C-SPARQL with RDFox set-up (3), the adapted streaming RDFox set-up (5) and the streaming Jena set-up (7). These missing measurements are caused by the systems running out of memory, causing them to stop evaluating the queries for the remainder of the scenario. The DIVIDE baseline set-up (1) has the lowest average total execution time from 960 seconds into the evaluation. Before this timestamp, the non-streaming RDFox set-up (4) is the quickest.

Figure 6 shows similar results for the real-time evaluation of the showering query. The same three set-ups run out of memory at a certain point, causing missing measurements for the remainder of the evaluation runs. Already after 550 seconds into the evaluation, the DIVIDE baseline set-up (1) has the lowest average total execution time.

Figure 7 shows similar results of the comparison of the real-time evaluation of DIVIDE with the real-time reasoning approaches, but for the brushing teeth query. The properties of the graph are similar to those of the graph presenting the results for the toileting query. In these results, only the non-streaming RDFox set-up (4) has no measurements for the full time course of the evaluation scenario due to the engine running out of memory.

In Appendix D, additional results of the evaluation runs over time are included. These results visualize the distribution of the total execution times for the different set-ups at different times during the evaluation runs.

Fig. 6.

Results of the comparison of the DIVIDE real-time query evaluation approach with real-time reasoning approaches, for the showering query. For each evaluation set-up, the results show the evolution over time of the total execution time from the generated event (either a windowed event in a streaming set-up or an incoming event in a non-streaming set-up) until the routine activity prediction as output of the final query. For all set-ups, measurements are shown for the processed event, either incoming or windowed, at every 10 seconds. All plotted execution times are averaged over the evaluation runs.

Fig. 7.

Results of the comparison of the DIVIDE real-time query evaluation approach with real-time reasoning approaches, for the brushing teeth query. For each evaluation set-up, the results show the evolution over time of the total execution time from the generated event (either a windowed event in a streaming set-up or an incoming event in a non-streaming set-up) until the routine activity prediction as output of the final query. For all set-ups, measurements are shown for the processed event, either incoming or windowed, at every 10 seconds. All plotted execution times are averaged over the evaluation runs.

9.3.Real-time evaluation of derived DIVIDE queries on a Raspberry Pi

Figure 8 shows the results of the evaluation of the DIVIDE set-up on the Raspberry Pi 3. These results visualize the distribution of the individual execution times of the RSP queries generated by DIVIDE with the C-SPARQL baseline set-up, for the toileting, showering and brushing teeth scenarios. For the toileting query, the average total execution time is 3,666 ms (SD 318 ms). This average number is 3,699 ms (SD 286 ms) and 3,001 ms (SD 174 ms) for the showering and brushing teeth scenarios, respectively.

Fig. 8.

Results of evaluating the DIVIDE real-time query evaluation approach with the C-SPARQL baseline set-up (1), on a Raspberry Pi 3, Model B. The results show the total execution time distribution over the engine’s runtime and multiple runs, for the toileting, showering and brushing teeth DIVIDE queries.