Mikko Rinne

Processing Heterogeneous RDF Events with Standing SPARQL Update Rules

SPARQL query language is targeted to search datasets encoded in RDF. SPARQL Update adds support of insert and delete operations between graph stores, enabling queries to process data in steps, have persistent memory and communicate with each other. When used in a system supporting incremental evaluation of multiple simultaneously active and collaborating queries SPARQL can define entire event processing networks. The method is demonstrated by an example service, which triggers notifications about the proximity of friends, comparing alternative SPARQL-based approaches. Observed performance in terms of both notification delay and correctness of results far exceed systems based on window repetition without extending standard SPARQL or RDF.

Efficient matching of SPARQL subscriptions using rete

Ubiquitous domains such as smart spaces, location-aware mobile systems, or internet-of-things are characterized by large and volatile sets of heterogeneous and independently behaving entities like devices, services, and other identified objects. This study focuses on efficient implementation of an event processing system to manage interaction among these entities. The approach is based on expressive semantic representations: information sharing in RDF and content-based publish/subscribe with SPARQL as the subscription language. SPARQL can be used to construct elaborate queries for detecting complex states resulting from receiving events produced by multiple interrelated entities. The notification system should aim at short notification times while simultaneously allowing high throughput of events. We study incremental matching of SPARQL queries on RDF data using Rete algorithm. The results obtained demonstrate that an efficient and fast semantic notification framework can be implemented by representing SPARQL queries and RDF triples as rules and facts in a Rete engine.

RFID-based logistics monitoring with semantics-driven event processing

In this paper a real-life counterfeit and theft detection scenario from pharmaceutical manufacturing is modelled using events encoded as XML and RDF. With Esper and Instans event processing platforms, the second one from the semantic web domain, the same task is configured and an experimental performance evaluation is carried out. Our results show that even though the starting points are very different, the same core task can be accomplished on both platforms. We provide quantitative performance comparisons that corroborate our analysis. For an understanding of what can be expected from each framework outside the core task, the differences between the two tools and their respective domains are qualitatively analysed.

Spaceify: a client-edge-server ecosystem for mobile computing in smart spaces

Spaceify is a novel edge architecture and an ecosystem for smart spaces --- a technology that extends the mobile user view of todays common space services (e.g., WiFi) to a richer portfolio of space-centric, localized services and space-interactive applications.

SPARQL update for complex event processing

Complex event processing is currently done primarily with proprietary definition languages. Future smart environments will require collaboration of multi-platform sensors operated by multiple parties. The goal of my research is to verify the applicability of standard-compliant SPARQL for complex event processing tasks. If successful, semantic web standards RDF, SPARQL and OWL with their established base of tools have many other benefits for event processing including support for interconnecting disjoint vocabularies, enriching event information with linked open data and reasoning over semantically annotated content. A software platform capable of continuous incremental evaluation of multiple parallel SPARQL queries is a key enabler of the approach.

Constructing Event Processing Systems of Layered and Heterogeneous Events with SPARQL

SPARQL was originally developed as a derivative of SQL to process queries over finite-length datasets encoded as RDF graphs. Processing of infinite data streams with SPARQL has been approached by using pre-processors dividing streams into finite-length windows based on either time or the number of incoming triples. Recent extensions to SPARQL can support interconnections of queries, enabling event processing applications to be constructed out of multiple incrementally processed collaborating SPARQL update rules. With more elaborate networks of queries it is possible to perform event processing on heterogeneous event formats without strict restrictions on the number of triples per event. Heterogeneous event support combined with the capability to synthesize new events enables the creation of layered event processing systems. In this paper we review the different types of complex event processing building blocks presented in literature and show their translations to SPARQL update rules through examples, supporting a modular and layered approach. The interconnected examples demonstrate the creation of an elaborate network of SPARQL update rules for solving event processing tasks.

Automatic Recognition of Public Transport Trips from Mobile Device Sensor Data and Transport Infrastructure Information

Automatic detection of public transport (PT) usage has important applications for intelligent transport systems. It is crucial for understanding the commuting habits of passengers at large and over longer periods of time. It also enables compilation of door-to-door trip chains, which in turn can assist public transport providers in improved optimisation of their transport networks. In addition, predictions of future trips based on past activities can be used to assist passengers with targeted information. This article documents a dataset compiled from a day of active commuting by a small group of people using different means of PT in the Helsinki region. Mobility data was collected by two means: (a) manually written details of each PT trip during the day, and (b) measurements using sensors of travellers’ mobile devices. The manual log is used to cross-check and verify the results derived from automatic measurements. The mobile client application used for our data collection provides a fully automated measurement service and implements a set of algorithms for decreasing battery consumption. The live locations of some of the public transport vehicles in the region were made available by the local transport provider and sampled with a 30-s interval. The stopping times of local trains at stations during the day were retrieved from the railway operator. The static timetable information of all the PT vehicles operating in the area is made available by the transport provider, and linked to our dataset. The challenge is to correctly detect as many manually logged trips as possible by using the automatically collected data. This paper includes an analysis of challenges due to missing or partially sampled information, and initial results from automatic recognition using a set of algorithms comparing measured trips with both live vehicle locations and static timetables. Improvement of correct recognitions is left as an ongoing challenge.