Towards processing and reasoning streams of events ...

Towards processing and reasoning streams of events in knowledge-driven manufacturing execution systems Borja Ramis Ferrer, Sergii Iarovyi, Andrei Lobov, José L. Martinez Lastra Tampere University of Technology, Tampere, Finland {borja.ramisferrer, sergii.iarovyi, andrei.lobov, jose.lastra}@tut.fi Abstract— The incessant need of the industry to optimize processes due to market demands derived in a huge investment on information communication technologies implementation during last decades, in the industrial automation domain. This caused the implementation of paradigms as service-oriented or event-driven architectures in factories, used for wide data integration. Moreover, the use of knowledge representation, within ontologies, permitted the description of system status in knowledge bases, which can be queried and updated at runtime. Due to the massive occurrence of events at any location of the enterprise, complex event processing (CEP) technologies can be used for anticipating facts that can compromise the production at shop floors. In fact, recent implementations on processing and reasoning streams of events in the Semantic Web can be applied also in the industrial automation domain because they combine CEP and SPARQL, which are technologies nowadays used by factory systems. This article describes how these technologies can support the study of the ontological system models evolution through time and an approach to bring predictability to current knowledge-based systems. Keywords—Complex Event Processing, ontologies, SPARQL; knowledge-based sysems, industrial automation.

I.

INTRODUCTION

Factory automation systems have been evolving rapidly during last decades, pushed by market needs and social changes. Distributed control systems emerged in the 80’s allowing the coordination of several networked Programmable Logic Controllers (PLCs) for providing control functions at machine level. This fact supposed an important advance in manufacturing systems because they became more flexible and time dramatically decreased when adapting shop floors to new manufacturing processes. Then, the growing of Information Communication Technologies (ICT) and, in parallel, the networking capabilities of industrial automation systems made possible the integration of heterogeneous data, which was generated and processed by different systems. This was possible within the implementation of the ISA-95 standard [1], which defines the concepts required for integration of dissimilar factory systems. Web Service (WS) enabled controllers that permit the implementation of the Service Oriented Architecture (SOA) paradigm [2] in actual production lines [3]. The use of SOA in manufacturing systems not only allows the control of processes

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through WS but also provides an easy and a robust solution for monitoring the status of systems at any system layer. Monitoring manufacturing systems is an essential part of industrial automation because it permits production engineers to have a real-time view of the system status. Moreover, the success of manufacturing processes is analyzed and tracked by companies with sets of Key Performance Indicators (KPIs), which also have been standardized by i.e. [4]. Among others, Enterprise resource planning (ERP) or Manufacturing Execution System (MES) improve its efficiency within implementations of SOA. On the other hand, actual Event Driven Architecture (EDA) implementations are related to SOA [5], forming the Eventdriven SOA. At device level, EDA may be implemented within the Device Profile for Web Services (DPWS) specification and its eventing mechanism: the WS-Eventing protocol [6]. Moreover, the concept of Complex Event Processing (CEP) has emerged in the factory automation domain for monitoring streams of events in real time [7]. Thus, CEP technologies allow manufacturing systems to react before facts that reduce the efficiency of the production [8, 9]. Other trend in the industrial automation domain is to include the use of semantic languages for representing knowledge of systems and also for allowing the development of knowledge-based systems, as presented in [10, 11]. These systems are approaches interoperability, monitoring and knowledge-driven solutions, which all are based on cross-layer communications. This research work is motivated by several studies as [12] and [13] that describes how to combine the concepts of CEP and querying KB systems for processing and reasoning streams of events in the Semantic Web. In fact, [12] presents an extension of the SPARQL language that allows bridging the gap between the background knowledge enriched with analysis of event streams and reasoning tasks. As recent manufacturing systems developments use these technologies, this article describes how to integrate them with the objective of processing and reasoning streams of events on run-time manufacturing processes. The rest of the paper is structured as follows: Section II presents the theoretical background of this research. Then, Section III describes how streams of events can be processed and reasoned in manufacturing systems by adapting recent

developments used in the Semantic Web. Afterwards, Section IV discusses potentials and benefits of the presented research work. Finally, Section V concludes the article. II.

THEORETICAL BACKGROUND

A. Complex event processing SOA and EDA are proposing new approach for process control in distributed solutions. Such concepts allow facilitating more complex systems via encapsulation of the business knowledge in subsystems, which isolates complexity of integral system. Recent advance of Cyber-Physical Systems (CPS) in the factory automation level enables application of the SOA and EDA concepts throughout whole industrial solution making it more modular, heterogeneous and distributed. The atomic element of communication in EDA systems is an event. Also single event may provide only information of limited volume and scope, the sets of events are providing significantly more information about system status and dynamics then each of event separately. For processing sequences and sets of events, the Event Processing concept is being employed. Event processing usually is defined on three levels Simple Event Processing (SEP), Event Stream Processing (ESP) and Complex Event Processing (CEP). SEP is an approach for analyzing of single events, and it provides little or no additional information besides the one provided in event related messages. ESP in turn may analyze sequences of events comparing them to defined patterns. ESP allows obtaining additional information in ordered sequence of events or event stream. Most complete approach of event processing is CEP. In CEP several streams of events are analyzed both as individual events and as event patterns, employing complex relationships between events. Beyond the analysis of events, event processing allows filtering, transformation and even creation of new events. The possibility to analyze individual events in event clouds and infer occurrence of complex events in distributed systems is one of the main benefits provided by [20-22]. Application of CEP in the domain of industrial automation is becoming particularly important in heterogeneous distributed automation systems. In 2011, researchers demonstrate in [5] the integration of different implementation of SOA employing CEP for a monitoring system. Possibility and benefits of application of CEP for energy monitoring systems are presented in [8]. Further applications of CEP for industrial automation are proposed in [23, 24], which are research work implementations belonging to the IMC-AESOP project1. The applications are related to energy management and implementation of a scalable monitoring system for industrial system. Although EDA and CEP provide significant benefits for automation domain, several obstacles are limiting their broad application. Most importantly is dissimilarity of messages available currently in the system events. In order to define the event analysis algorithm, user is often forced to provide information not only on data sources, but also on non-message 1

http://imc-aesop.eu/



and data formats. Furthermore, reasoning over the simple messages limits the scope of data descriptions to value time comparisons, not allowing deeper correlation of data. B. Industrial automation knowledge-driven solutions The amount of data available for contemporary manufacturing systems is being higher than ever before and due to advance in ICT and CPS is expected to become even bigger. Besides the vast amounts of data in the system more knowledge is required to keep such systems operational. The Knowledge-Driven approach aims to include this aspect of industrial automation systems in the solution. In the Knowledge-Driven system the problem of persistence and manipulation of information about the system is being addressed by the Knowledge Representation (KR) concept [35, 36]. KR allows representing the knowledge in form that can be used by a machine to automate the tasks, which are becoming more complex for human operators due to increased volume of data. Several formalisms may be employed to implement KR for system status representation, such as rules, frames, linked data or ontologies. Nevertheless, in recent years, linked data and ontologies are being leading in this domain. Meanwhile linked data provides rather simple approach of definition or relations between pieces of data it allows restricted knowledge capabilities. More sophisticated reasoning capabilities may be achieved in ontologies, which besides categorization of knowledge are providing axioms defining such categories. Currently, among implementation of the ontology languages, according to a survey [25] dominant ones are based on Resource Definition Framework (RDF) format [32], which is an XML-based language. RDF-based languages describe information in form of subject-predicate-object triples and belong to the W3C web standards [26, 27]. In fact, RDF-based models are at the end RDF graphs that in turn are a set of RDF triples. An important feature of ontologies is capability to be queried. Generally, for querying RDF-based documents SPARQL [33] querying language may be applied. This language allows extracting data from ontology if it maps description provided in query. Such descriptions are relatively easy to comprehend due to their logical background. Also exists SPARQL Update language extension [34], which allows not only reading the data from ontology, but also creates, update or delete. The most complete set of ontological features is presented in the Web Ontology Language (OWL) [27]. Different restrictions in the approach concept representation are employed in different dialects of OWL, leading to different level of data abstraction. Three most prominent dialects of OWL are: OWL-Lite, OWL-Description Logic (DL) and OWL-Full. These different dialects allow implying the knowledge not being introduced in the system explicitly by analyzing available data, but based on defined axioms. Combination of such properties of ontologies as a formalism for KR allows exploitation of it in different industrial applications, which is proven in multiple research

works [10, 11, 29-31]. Knowledge-Driven solutions provide a significant level of flexibility to the system and allow including more intelligence in industrial systems behavior.

label. Hence, notation for temporal triples is (s, p, o)[t], where s, p and o are, respectively, subject, predicate and object of a RDF triple and t is the timestamp of such triple. It should be noted that t is meant to be a natural number.

C. SPARQL languages for reasoning streams of events Short time ago researchers on the Semantic Web identified the need of reasoning upon data that changes quickly. In order to cope this requirement, the integration of CEP and reasoning technologies was proposed. In this scenario, emerged the concept of Streaming Reasoning for the Semantic Web [17]. In fact, E. Della Valle et al. argued in 2009 [17] that a tremendous amount of innovation was necessary to make stream reasoning feasible.

Then, by the addition of a temporal label on each triple, the time of model instance relations can be monitored. For depicting temporal changes on ontologies, Fig. 1 shows a representation the evolution of triples over time in RDF graphs.

Recently, software engineers have presented novel SPARQL extension languages, which can be used nowadays for continuous queries over streams of RDF data [12-16]. In fact, Continuous SPARQL (C-SPARQL) and Event Processing SPARQL (EP-SPARQL) are two new languages that have emerged for bridging the gap between knowledge of systems and streams of events processing. It should be noted that formal semantics, language syntax and detailed descriptions of C-SPARQL and EP-SPARQL are found in [15] and [12], respectively. In fact, it is observable that the developers of ETALIS engine [28], which is an open source system for CEP, implemented the EP-SPARQL language. On the other hand, a survey on semantic complex event processing for social media monitoring is presented in [16], which also reviews another CEP language: SASE [18]. Nevertheless, [16] states that SASE does not explicitly support Semantic Web streams. The main novelty of C-SPARQL and EP-SPARQL is the addition of RDF streams to the standard data types that are supported by SPARQL. This allows the consideration of time in queries enabling the evaluation of temporal RDF graphs during system execution. Therefore, these new languages offer an improvement of monitoring and pattern detection mechanisms, which are applied in industrial automation, financial, mobile or health systems, among others. In the case of factory automation domain, advances on SPARQL, which is widely used for querying KBs, opens a new research line plenty of possibilities and challenges as the enhancement of monitoring mechanisms of manufacturing systems, allowing the anticipation to problems in production processes. III.

STREAM REASONING IN THE INDUSTRIAL AUTOMATION DOMAIN

A. Monitoring the evolution of system model over time One of the benefits of processing and reasoning streams of events in manufacturing execution systems, discussed in next Section IV, is monitoring the evolution of ontological system models over time. As it can be concluded from previous explanations and stated also in [13], C-SPARQL and EPSPARQL are closely linked to ontologies due to the fact that both are extensions of SPARQL, which is used for querying RDF graphs. According to the basic definitions and semantics of temporal RDF graphs presented in [19], time-stamped or temporal triples are RDF triples that incorporate a temporal



Fig. 1: Evolution of triples over time in RDF graphs representation

The stack of previous figure represents the ontology status at different temporal labels. In other words, each plane of the stack is meant to contain all the triples at certain time. Aiming the simplification of the figure, meanwhile all rhombuses represent different graph subjects and objects (instances); all squares represent the same predicate (or property), which links instances. In fact, it is shown that the plane at t1 and the one at t9 have the same number of instances, which also could have changed. It is possible that a triple does not change between different timestamps. Indeed, [19] describes that a temporal label [t] can be noted as an interval [t1, t2], which completes the final notation for temporal triple: {(s, p, o) | [t1 ≤ t ≤ t2]}. Briefly put, corresponding timestamps of starting and finishing a RDF triple are represented also in temporal triples. As an example, Fig. 1 shows that the bottom triple of the t1 plane remains on t4 plane. Assuming that the triple remains in t2 and t3, and considering also that the triple does not anymore exist in t5, it can be guaranteed that the notation of this temporal triple will be: {(s, p, o)[t1, t4]}. B. An EP-SPARQL application in manufacturing systems As explained in Section II, actual industrial automation systems are forced to manage a huge amount of events that occur at different points of organizations. Due to the implementation of cross-layer communication, all events can be handled independently on which location they take place. This research intends to explore the applications of recent developments in stream reasoning and CEP processing. Therefore, the work presented in this section is based on some previous cited works as the one presented by D. Anicic et al. in [28], which present some examples for processing and reasoning streams of events in the Semantic Web utilizing the ETALIS engine. One of the described examples is an application of EP-SPARQL concerned in a traffic management system.

In order to propose an example in the industrial automation domain, some new MES service operations are considered. Such operations are complex tasks that require scheduling and monitoring the introduction of pallets (or containers) in transport systems of assembly lines. The importance of this service is huge because it controls the amount of pallets flowing in the production line for not lowering the production. Fig. 2 presents a Festo2 MPS assembly line.

Fig. 2: Festo line in Tampere University of Technology FAST-Lab3 facilities

As it can be seen in previous diagram, containers are placed in a central transport system that brings pallets to different manufacturing cells, which processes the parts transported by pallets. One of the issues that the MES is aware of is controlling the amount of pallets that are flowing within the system. In fact, when a pallet stops in a cell for processing its corresponding part, other containers cannot bypass that location due to the physical distribution of the system. Moreover, if a pallet cannot deliver a part in a cell because it is in busy status at the moment of pallet arrival, the pallet must make the system loop once more until the part is delivered. Thus, if there are a large amount of pallets in the system, the process can have huge delays, undesired for the overall production time. The ongoing research in the eScop project4 and implement new concepts for approaching novel knowledge-driven manufacturing execution systems that manages the system knowledge on ontologies [36], which are queried on system execution runtime. For this article, the MES system, which controls the events of the shown production line in Fig. 2, orders the introduction of a new pallet in the system when the transport system can handle more containers. Orders are sent after processing SELECT SPARQL queries that retrieve a list of pallets flowing in the production line. Then, processing the query results, the decision of pallet addition is made. But within the possibility of combining CEP and SPARQL, the management on line containers, can be done in another efficient manner, using EP-SPARQL. This is possible because, as the employed ontological model is RDF-based, the system knowledge are queried within SPARQL that is the extended language by EP-SPARQL. Assuming that (1) MES can handle busy events generated by cells that cannot accept a part transported by a container and 2

http://www.festo.com/ http://www.tut.fi/en/fast/ 4 http://www.escop-project.eu/ 3



(2) three busy events in less than 10 minutes must stop the introduction of new pallets, the following query in EPSPARQL returns a true/false response to the MES for ordering the introduction of new pallets in the system: PREFIX mso: PREFIX xsd: PREFIX rdfs: ASK WHERE {?Cell mso:Status mso:busy } SEQ {?Cell mso:Status mso:busy } SEQ {?Cell mso:Status mso:busy } FILTER (getDURATION()