StreamJess: a stream reasoning framework for water quality monitoring

Jajaga, Edmond; Ahmedi, Lule; Ahmedi, Figene

doi:10.1504/ijmso.2016.083507

Cited by 7 publications

(3 citation statements)

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“…On the other hand, there are some works where water quality is dealt with semantic technologies. In Jajaga et al (2016), the authors provided details about a system that handles stream data and detects inadequate water quality statuses. The system also recognized the potential sources of pollution by extending a previously developed ontology (Jajaga et al 2015).…”

Section: Related Workmentioning

confidence: 99%

Characterizing water quality datasets through multi-dimensional knowledge graphs: a case study of the Bogota river basin

Diaz

Vilches‐Blázquez

2022

Journal of Hydroinformatics

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The world is transforming into a predominantly urban space, and it entails that cities have to be ready to provide services, for instance, to ensure availability and sustainable management of water and sanitation for all. In this scenario, the water quality evaluation has a crucial role and often needs multiple sources segregated. Our purpose is to build bridges between these data silos to provide an integrated and interoperable view, where different datasets can be provided and combined through knowledge graphs in order to characterize water quality. This work shows the quality of the Bogota river basin's water bodies by analyzing physicochemical and biological properties and using spatio-temporal and legal elements. So, our knowledge graphs allow us to discover what, when, and where infractions happened on water quality in a river basin of the most populated cities of Latin America during a critical period (2007–2013), highlighting the presence of high values of suspended solids and nitrites, lower amounts of dissolved oxygen, and the worst water quality during the driest periods (appearing until a maximum of 63 infractions in a year).

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Section: Related Workmentioning

confidence: 99%

Characterizing water quality datasets through multi-dimensional knowledge graphs: a case study of the Bogota river basin

Diaz

Vilches‐Blázquez

2022

Journal of Hydroinformatics

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“…Some attempts were even focused on building a complete adaptive application based on ontology and defining rules and inferences on such rules on a semantic level [31]. Current studies have even attempted reasoning on more dynamic data, such as stream sensor data [32] or dynamic Semantic Web data [33].…”

Section: Ontologies For Web Personalizationmentioning

confidence: 99%

Web personalization issues in big data and Semantic Web: challenges andopportunities

Raufi¹,

Ismaili²,

Ajdari³

et al. 2019

Turk J Elec Eng & Comp Sci

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Web personalization is a process that utilizes a set of methods, techniques, and actions for adapting the linking structure of an information space or its content or both to user interaction preferences. The aim of personalization is to enhance the user experience by retrieving relevant resources and presenting them in a meaningful fashion. The advent of big data introduced new challenges that locate user modeling and personalization community in a new research setting. In this paper, we introduce the research challenges related to Web personalization analyzed in the context of big data and the Semantic Web. This paper also introduces some models and approaches that can bridge the gap between the two. Future challenges and opportunities related to Web personalization, analyzed from the big data and Semantic Web perspective, are also presented. The research challenges outlined in this paper involve the scrutability of user models in personalization, generic personalization, meta-personalization, open corpus personalization, and semantic data modeling.

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“…In 2009, Stream Reasoning was defined as an "unexplored yet high impact research area". A number of its implementations are currently in place including C-SPARQL [7], StreamRule [8], StreamJess [9], C-SWRL [10], ETALIS, EP-SPARQL, etc.…”

Section: Introductionmentioning

confidence: 99%

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

Aliti¹,

Jajaga²,

Sevrani³

2018

IJBTE

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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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StreamJess: a stream reasoning framework for water quality monitoring

Cited by 7 publications

References 0 publications

Characterizing water quality datasets through multi-dimensional knowledge graphs: a case study of the Bogota river basin

Characterizing water quality datasets through multi-dimensional knowledge graphs: a case study of the Bogota river basin

Web personalization issues in big data and Semantic Web: challenges andopportunities

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

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