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

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.

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

2017 ◽  
Vol 11 (03) ◽  
pp. 391-409
Author(s):  
Edmond Jajaga ◽  
Lule Ahmedi
Keyword(s):  
Semantic Web ◽  
Web Applications ◽  
Processing System ◽  
Water Quality Monitoring ◽  
Stream Data ◽  
New Paradigm ◽  
Data Stream Management ◽  
Stream Management ◽  
Closed World ◽  
Stream Reasoning

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.

EFFICIENTLY MINING RECENT FREQUENT PATTERNS OVER ONLINE TRANSACTIONAL DATA STREAMS

2009 ◽  
Vol 19 (05) ◽  
pp. 707-725 ◽  
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.

SSC: cloud-based data stream management in distributed environments

2016 ◽  
Vol 9 (3) ◽  
pp. 171
Author(s):  
Kok Leong Ong ◽  
Andrzej Goscinski ◽  
Yuzhang Han ◽  
Peter Brezany ◽  
Zahir Tari ◽  
...  
Keyword(s):  
Data Stream ◽  
Distributed Environments ◽  
Data Stream Management ◽  
Stream Management

scholarly journals IoTSAS: An Integrated System for Real-Time Semantic Annotation and Interpretation of IoT Sensor Stream Data

Computers ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 127
Author(s):  
Besmir Sejdiu ◽  
Florije Ismaili ◽  
Lule Ahmedi
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Contextual Information ◽  
Semantic Annotation ◽  
Weather Conditions ◽  
Water Quality Monitoring ◽  
Integrated System ◽  
Quality Monitoring ◽  
Stream Data ◽  
Semantic Annotations

Sensors and other Internet of Things (IoT) technologies are increasingly finding application in various fields, such as air quality monitoring, weather alerts monitoring, water quality monitoring, healthcare monitoring, etc. IoT sensors continuously generate large volumes of observed stream data; therefore, processing requires a special approach. Extracting the contextual information essential for situational knowledge from sensor stream data is very difficult, especially when processing and interpretation of these data are required in real time. This paper focuses on processing and interpreting sensor stream data in real time by integrating different semantic annotations. In this context, a system named IoT Semantic Annotations System (IoTSAS) is developed. Furthermore, the performance of the IoTSAS System is presented by testing air quality and weather alerts monitoring IoT domains by extending the Open Geospatial Consortium (OGC) standards and the Sensor Observations Service (SOS) standards, respectively. The developed system provides information in real time to citizens about the health implications from air pollution and weather conditions, e.g., blizzard, flurry, etc.

Data Stream Management Systems and Architectures

2007 ◽  
pp. 51-71 ◽  
Author(s):  
M. A. Hammad ◽  
T. M. Ghanem ◽  
W. G. Aref ◽  
A. K. Elmagarmid ◽  
M. F. Mokbel
Keyword(s):  
Data Stream ◽  
Management Systems ◽  
Data Stream Management ◽  
Stream Management ◽  
Data Stream Management Systems
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