Complex Event Processing on Linked Stream Data

Abstract

Social networks and Sensor Web technologies typically generate a massive amount of data published as streams. In order to give these streams a meaningful sense and enrich them with semantic descriptions, the concept of Linked Stream Data (LSD) has emerged. However, to support a wide range of LSD scenarios and queries comprehensive solutions providing not only classic data stream operators such as windows, but also for processing of complex events, linking of (static) datasets, and scalable processing are required. In this paper, we present our approach for processing LSD and addressing these requirements. In contrast to existing LSD engines relying on streaming extensions to SPARQL, our PipeFlow system is a (relational) dataflow language and engine providing support for complex event processing (CEP) and a few dedicated operators for RDF data. We describe this language and particularly the CEP model as well as the system architecture for parallel CEP and LSD processing by exploiting partitioning techniques for cluster environments. Finally, we report results from experiments evaluating our system in comparison to existing LSD engines.

Big Data Semantics

“…Figure 1 offers a synthetic view on the pipeline adopted as a reference for this paper. Markl [14] x x Ardagna et al [34] x x x x x x Azzini et al [23] x x Smith et al [26] x x Liao et al [35] x x Duggan et al [36] x x Sowmya et al [37] x x Zhou et al [38] x x Akoush et al [39] x x Glavic [40] x x Berti-Equille et al [41] x x Kläs et al [42] x x Daiber et al [43] x x Shin et al [44] x x x Chiticariu et al [45] x x Fuhring et al [46] x x Bondiombouy et al [47] x x Bergamaschi et al [48] x x Ramakrishnan et al [49] x x Masseroli et al [50] x x Scannapieco et al [51] x x Gualtieri et al [52] x x Liu et al [53] x x Gulzar et al [54] x x De Wit [55] x x Zardetto et al [56] x x Gonzalez et al [57] x x Junghanns et al [58] x x Yu et al [59] x x You et al [60] x x Hagedorn et al [61] x x Kornacker et al [62] x x Costea et al [63] x x Schätzle et al [64] x x Cudré-Mauroux et al [65] x x Appice et al [66] x x Khare2015 et al [67] x x Poggi et al [68] x x Um et al [69] x x Poole et al [16] x x Smith et al [26] x x Gies...…”

“…Luckham [156] introduces Complex Event Processing (CEP) by defining complex events which are correlated among each other. Saleh et al [61] apply the data aggregation approach of CEP to data streams. Process Mining (PM) is a process-centric management technique bridging the gap between data mining and traditional model-driven Business Process Management (BPM) [157,158].…”

Performance evaluation of linked stream data processing engines for situational awareness applications

“…They address the challenges in querying data streams coming from dynamic data sources, such as those in smart cities applications (eg, health care and transportation roads). In order to enrich the amount of data streams published, those engines extend RDF (Resource Description Framework) streams from static datasets to express continuous queries over linked data streams . Moreover, each engine has its own CQL (Continuous Query Language) to processes in real‐time arrival event streams from connected sensors.…”

“…In order to enrich the amount of data streams published, those engines extend RDF (Resource Description Framework) streams from static datasets to express continuous queries over linked data streams. 20 Moreover, each engine has its own CQL (Continuous Query Language) 21 to processes in real-time arrival event streams from connected sensors. Furthermore, to support a wide range of semantic data and queries processing, there are a number of RDF Stream solutions, such as those used in CQLES, C-SPARQL, INSTANS, 22 and ETALIS/EP-SPARQL.…”