Using Semantic Technology to Tame the Data Variety Challenge

Horrocks, Ian; Giese, Martin; Kharlamov, Evgeny; Waaler, Arild

doi:10.1109/mic.2016.121

Cited by 57 publications

(27 citation statements)

References 1 publication

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“…The Optique platform [47] is an end-to-end OBDA solution, i.e., it supports the whole OBDA cycle from deployment to query-answering visualisation. Optique platform integrates a number of existing systems and provides several new components.…”

Section: The Optique Platform For Siemensmentioning

confidence: 99%

Semantic access to streaming and static data at Siemens

Kharlamov

Mailis

Mehdi

et al. 2017

Journal of Web Semantics

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We present a description and analysis of the data access challenge in Siemens Energy. We advocate Ontology Based Data Access (OBDA) as a suitable Semantic Web driven technology to address the challenge. We derive requirements for applying OBDA in Siemens, review existing OBDA systems and discuss their limitations with respect to the Siemens requirements. We then introduce the Optique platform as a suitable OBDA solution for Siemens. The platform is based on a number of novel techniques and components including a deployment module, BootOX for ontology and mapping bootstrapping, a query language STARQL that allows for a uniform querying of both streaming and static data, a highly optimised backend, ExaStream, for processing such data, and a query formulation interface, OptiqueVQS, that allows to formulate STARQL queries without prior knowledge of its formal syntax. Finally, we describe our installation and evaluation of the platform in Siemens.

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Section: The Optique Platform For Siemensmentioning

confidence: 99%

Semantic access to streaming and static data at Siemens

Kharlamov

Mailis

Mehdi

et al. 2017

Journal of Web Semantics

Self Cite

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“…Horrocks et al propose an ontology-based data access (OBDA) solution for diverse varieties of data management. They describe their efforts in the context of managing disparate Siemens Energy Services datasets; see [43] for further discussion.…”

Section: Related Workmentioning

confidence: 99%

EpiK: A Knowledge Base for Epidemiological Modeling and Analytics of Infectious Diseases

Hasan

Fox

Bisset

et al. 2017

J Healthc Inform Res

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Computational epidemiology seeks to develop computational methods to study the distribution and determinants of health-related states or events (including disease), and the application of this study to the control of diseases and other health problems. Recent advances in computing and data sciences have led to the development of innovative modeling environments to support this important goal. The datasets used to drive the dynamic models as well as the data produced by these models presents unique challenges owing to their size, heterogeneity and diversity. These datasets form the basis of effective and easy to use decision support and analytical environments. As a result, it is important to develop scalable data management systems to store, manage and integrate these datasets. In this paper, we develop EpiK-a knowledge base that facilitates the development of decision support and analytical environments to support epidemic science. An important goal is to develop a framework that links the input as well as output datasets to facilitate effective spatio-temporal and social reasoning that is critical in planning and intervention analysis before and during an epidemic. The data management framework links modeling workflow data and its metadata using a controlled vocabulary. The metadata captures information about storage, the mapping between the linked model and the physical layout, and relationships to support services. EpiK is designed to support agent-based modeling and analytics frameworks-aggregate models can be seen as special cases and are thus supported. We use semantic web technologies to create a representation of the datasets that encapsulates both the location and the schema heterogeneity. The choice of RDF as a representation language is motivated by the diversity and growth of the datasets that need to be integrated. A query bank is developed-the queries capture a broad range of questions that can be posed and answered during a typical case study pertaining to disease outbreaks. The queries are constructed using SPARQL Protocol and RDF Query Language (SPARQL) over the EpiK. EpiK can hide schema and location heterogeneity while efficiently supporting queries that span the computational epidemiology modeling pipeline: from model construction to simulation output. We show that the performance of benchmark queries varies significantly with respect to the choice of hardware underlying the database and resource description framework (RDF) engine.

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“…They utilize semantic web technology, in particular to ontology to enable more accurate and intelligent knowledge management. This article [5] shows how data analytics meant to be unchallenging with the use of semantic technology. Semantic technology offers a solution that addresses scalability and usability issues.…”

Section: Literature Surveymentioning

confidence: 99%

A Survey on Cognitive computing using Semantic Technology

Shah¹,

Shridevi²

2017

EAI Endorsed Transactions on Cloud Systems

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The sheer volume of data being generated by the resources is creating a form of cognitive overload for enterprises. The data will be useful to multiple processes in enterprises. Learning and understanding natural language for each process increases computer work in the organization. Enterprise Cognitive Computing (ECC) with the help of Semantic Technology generates structured and organized data for accurate decision making in enterprises. For developing ECC system there are many implementation challenges that need to resolve. ECC comprises Machine Learning, Artificial Intelligence and Question Answering machine that uses natural language processing technologies. These emerging technologies need to customize or combined with complementary solutions as semantic knowledge graphs, depending on the use case. This paper addresses problems of Enterprises that use unstructured data and how this problem can be resolved using semantic technology tools.

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Using Semantic Technology to Tame the Data Variety Challenge

Cited by 57 publications

References 1 publication

Semantic access to streaming and static data at Siemens

Semantic access to streaming and static data at Siemens

EpiK: A Knowledge Base for Epidemiological Modeling and Analytics of Infectious Diseases

A Survey on Cognitive computing using Semantic Technology

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