Stream Reasoning is the combination of reasoning techniques with data streams. In this paper, we present our approach to enable rule-based reasoning on semantic data streams in a distributed manne

Albeladi, Rehab

Gibbins, Nicholas

Martinez, Kirk

English

Southampton (e-Prints Soton)

Distributed Stream Reasoning Rehab Albeladi, Kirk Martinez, Nicholas Gibbins  Electronics and Computer Science, University of Southampton, UK {raab1g09,  km, nmg }@ecs.soton.ac.uk Abstract. Stream Reasoning is the combination of reasoning techniques with data  streams.  In  this  paper,  we  present  our  approach  to  enable  rule-based reasoning on semantic data streams in a distributed manner.  Data streams are being continually generated in diverse application domains such as traffic monitoring, smart buildings, and so on. Continuous processing of such data has been intensively investigated in the database community, where a special class of management systems [1][2] has been introduced to perform on-the-fly processing of data  streams.  However,  these  data  streams  lack  standard  formats,  which  make interoperability a real challenge. On the other hand, Semantic Web data has well-defined  meanings  and  a  number  of  semantic  formats  have  been  standardised. Semantic reasoners have been developed that can perform complex reasoning tasks on this data. Nevertheless, reasoning upon streaming data has received far less attention than reasoning upon static data. Stream Reasoning is the area that aims to combine reasoning techniques with data streams [3]. Research  in  this  area  mainly  focuses  on  extending  SPARQL  to  process  RDF streams. We focus more on the infrastructure of the reasoning process. Our approach enables reasoning in a continuous manner using low level operators; this approach differs from that in [4], in which each query is split into a static and a dynamic part and the dynamic part is passed to a DSMS system. In the implementation of our stream  reasoner,  we  have  focussed  on  the  two  main  issues  of  reasoning  and distribution. Stream Reasoning. To enable the rule-based reasoning process, we use the RETE algorithm [5] for pattern matching. Rules are translated into RETE networks of nodes. The nodes represent different operators and the data flows between these nodes. The tree-like  network  divides  the  matching  process  into  multiple  steps  that  perform different  checks,  so  if  a  data  element  does  not  match  the  first  node,  it  is  simply discarded  and  does  not  complete  its  way  through  the  network.  A  typical  RETE network has two types of node: filter (or alpha) nodes, and join (or beta) nodes. A filter node is similar to the select operation in query languages; it only propagates statements that match its condition. On the other hand, a join node is responsible for joining  some  data  elements  of  its  two  input  streams  depending  on  a  specified condition. Each join node manages a time-based or tuple-based sliding window on each stream input. A prototype RDFS reasoner for RDF data streams has been fully implemented, combining features from both reasoning techniques and stream processing techniques; it  performs  the  inference  task  as  a  rule  engine  using  a  Rete  network,  while  the implemented  Rete  network  performs  some  DSMS  operations,  such  as  converting streams into relations by using the sliding window technique. The system is fed by RDF streams, it matches them against the RDFS entailment rules, and produce new sets of data in a continuous manner. In our initial evaluation, we have been able to demonstrate  the  tradeoff  between  completeness  and  execution  time  by  varying window sizes. Distribution. For efficient processing of large volume data, scalability is a major concern.  Distributed  processing  of  data  streams  enables  more  scalable  and  fault-tolerant  systems,  so  we  distribute  our  reasoning  networks  using  the  eXtensible Messaging and Presence Protocol (XMPP). We have chosen XMPP for its push based distribution style which satisfies the real-time requirement of streaming applications with minimal latency, and for its ease of integration with Web technologies. In order to minimise network traffic, we use a graph-based definition for the RDF stream data type. Instead of triples, we define the RDF stream as an ordered sequence of RDF graphs associated with a time element. This can achieve other advantages besides being more efficient in terms of transportation; some queries can be evaluated by viewing the graph as a single record, and data provenance can be tracked more easily at a graph level.  We  have  built  a  prototype  system  that  can  process  RDF  data  streams  using distributed RETE networks, in which nodes are distributed in multiple machines and can communicate with each other using the XMPP in a publish/subscribe pattern, and are now working on combining this system with our previous reasoner to perform continuous reasoning on streaming RDF data in a distributed manner. References 1.  Abadi, D., Carney, D., Çetintemel, U., Cherniack, M., Convey, C., Lee, S. Stonebraker, M., Tatbul,  N.,  Zdonik,  S.:  Aurora:  A  New  Model  and  Architecture  for  Data  Stream Management. The VLDB Journal, 12(2), 120--139 (2003) 2.  Arasu, A., Babcock, B., Babu, S., Datar, M., Ito, K., Motwani, R., Nishizawa, I., Srivastava, U., Thomas, D., Varma, R., Widom, J.: STREAM: The Stanford Stream Data Manager, IEEE Data Engineering Bulletin, 26 (2003) 3.  Della Valle, E., Ceri, S., Barbieri, D.F., Braga, D., Campi, A.: A First Step Towards Stream Reasoning. In: Domingue, J., Fensel, D., Traverso, P. (eds.) FIS 2008. LNCS, vol. 5468, pp. 72--81. Springer, Heidelberg (2009)   4.  Barbieri,  D.,  Braga,  D.,  Ceri,  S.,  Grossniklaus,  M.:  An  execution  environment  for  C-SPARQL queries. In: 13th International Conference on Extending Database Technology. ACM, Lausanne, Switzerland (2010) 5.  Forgy,  C.:  Rete:  A  Fast  Algorithm  for  the  Many  Pattern/Many  Object  Pattern  Match Problem. Artificial Intelligence, 19, 17--37 (1982)  

An execution environment for CSPARQL queries. In:

Aurora: A New Model and Architecture for Data Stream Management.

Rete: A Fast Algorithm for the Many Pattern/Many Object Pattern Match Problem.

STREAM: The Stanford Stream Data Manager,

Distributed stream reasoning

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.370.2305

Distributed stream reasoning

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