C-SWRL: A Unique Semantic Web Framework for Reasoning Over Stream Data

The synergy of Data Stream Management Systems and Semantic Web applications has steered towards a new paradigm known as Stream Reasoning. The Semantic Web standards for knowledge base modeling and querying, namely RDF, OWL and SPARQL, has extensively been used by the Stream Reasoning community. However, the Semantic Web rule languages, such as SWRL and RIF, have never been used in stream data applications. Instead, different non-Semantic Web rule systems have been approached. Since RIF is primarily intended for exchanging rules among systems, we focused on SWRL applications with stream data. This proves difficult following the SWRL’s open world semantics. To overcome SWRL’s expressivity issues we propose an infrastructure extension, which will enable SWRL reasoning with stream data. Namely, a query processing system, such as C-SPARQL, was layered under SWRL to support closed-world and time-aware reasoning. Moreover, OWLAPI constructs were utilized to enable non-monotonicity, while SPARQL constructs were used to enable negation as failure. Water quality monitoring was used as a validation domain of the proposed system.

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A need for an integrative security model for semantic stream reasoning systems

International Journal of Business & Technology ◽

10.33107/ijbte.2018.6.3.01 ◽

2018 ◽

Vol 6 (3) ◽

pp. 1-4

Author(s):

Admirim Aliti ◽

Edmond Jajaga ◽

Kozeta Sevrani

Keyword(s):

Semantic Web ◽

Service Providers ◽

Water Quality Monitoring ◽

Security Model ◽

Stream Data ◽

Data Stream Management ◽

Typical Application ◽

Stream Management ◽

Reasoning Systems ◽

Stream Reasoning

State-of-the-art security frameworks have been extensively addressing security issues for web resources, agents and services in the Semantic Web. The provision of Stream Reasoning as a new area spanning Semantic Web and Data Stream Management Systems has eventually opened up new challenges. Namely, their decentralized nature, the metadata descriptions, the number of users, agents, and services, make securing Stream Reasoning systems difficult to handle. Thus, there is an inherent need of developing new security models which will handle security and automate security mechanisms to a more autonomous system that supports complex and dynamic relationships between data, clients and service providers. We plan to validate our approach on a typical application of stream data, on Wireless Sensor Networks (WSNs). In particular, WSNs for water quality monitoring will serve as a case study. The paper describes the initial findings and research plan for building a consistent security model for stream reasoning systems.

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EFFICIENTLY MINING RECENT FREQUENT PATTERNS OVER ONLINE TRANSACTIONAL DATA STREAMS

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194009004325 ◽

2009 ◽

Vol 19 (05) ◽

pp. 707-725 ◽

Cited By ~ 1

Author(s):

HUI CHEN

Keyword(s):

Data Mining ◽

Data Stream ◽

Frequent Patterns ◽

Stream Data ◽

Data Stream Management ◽

Online Data ◽

Stream Data Mining ◽

Network Traffic Analysis ◽

Stream Management ◽

Performance Results

Recent emerging applications, such as network traffic analysis, web click stream mining, power consumption measurement, sensor network data analysis, and dynamic tracing of stock fluctuation, call for study of a new kind of data, stream data. Many data stream management systems, prototype systems and software components have been developed to manage the streams or extract knowledge from stream data. Mining frequent patterns is a foundational job for the methods of data mining and knowledge discovery. This paper proposes an algorithm for mining the recent frequent patterns over an online data stream. This method uses RFP-tree to store compactly the recent frequent patterns of a stream. The content of each transaction is incrementally updated into the pattern tree upon its arrival by scanning the stream only once. Moreover, the strategy of conservative computation and time decaying model are used to ensure the correctness of the mining results. Finally, the performance results of extensive simulation show that our work can reduce the average processing time of stream data element and it is superior to other analogous algorithms.

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ITAIPU DATA STREAM MANAGEMENT SYSTEM - A Stream Processing System with Business Users in Mind

Proceedings of the Third International Conference on Software and Data Technologies Special Session on Applications in Banking and Finance ◽

10.5220/0001882000540064 ◽

2008 ◽

Keyword(s):

Management System ◽

Data Stream ◽

Stream Processing ◽

Processing System ◽

Data Stream Management ◽

Stream Management ◽

System A ◽

Data Stream Management System

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s2p: Provenance Research for Stream Processing System

Applied Sciences ◽

10.3390/app11125523 ◽

2021 ◽

Vol 11 (12) ◽

pp. 5523

Author(s):

Qian Ye ◽

Minyan Lu

Keyword(s):

Data Storage ◽

Stream Processing ◽

Processing System ◽

Coarse Grained ◽

Stream Data ◽

Related Data ◽

Dsp System ◽

Provenance Research ◽

Dsp Systems ◽

Abnormal Results

The main purpose of our provenance research for DSP (distributed stream processing) systems is to analyze abnormal results. Provenance for these systems is not nontrivial because of the ephemerality of stream data and instant data processing mode in modern DSP systems. Challenges include but are not limited to an optimization solution for avoiding excessive runtime overhead, reducing provenance-related data storage, and providing it in an easy-to-use fashion. Without any prior knowledge about which kinds of data may finally lead to the abnormal, we have to track all transformations in detail, which potentially causes hard system burden. This paper proposes s2p (Stream Process Provenance), which mainly consists of online provenance and offline provenance, to provide fine- and coarse-grained provenance in different precision. We base our design of s2p on the fact that, for a mature online DSP system, the abnormal results are rare, and the results that require a detailed analysis are even rarer. We also consider state transition in our provenance explanation. We implement s2p on Apache Flink named as s2p-flink and conduct three experiments to evaluate its scalability, efficiency, and overhead from end-to-end cost, throughput, and space overhead. Our evaluation shows that s2p-flink incurs a 13% to 32% cost overhead, 11% to 24% decline in throughput, and few additional space costs in the online provenance phase. Experiments also demonstrates the s2p-flink can scale well. A case study is presented to demonstrate the feasibility of the whole s2p solution.

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