From Business Intelligence to semantic data stream managementFuture Generation Computer Systems

About

Authors
Marie-Aude Aufaure, Raja Chiky, Olivier Curé, Houda Khrouf, Gabriel Kepeklian
Year
2015
DOI
10.1016/j.future.2015.11.015
Subject
Computer Networks and Communications / Hardware and Architecture / Software

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Text

Accepted Manuscript

From Business Intelligence to semantic data stream management

Marie-Aude Aufaure, Raja Chiky, Olivier Cure´, Houda Khrouf,

Gabriel Kepeklian

PII: S0167-739X(15)00363-5

DOI: http://dx.doi.org/10.1016/j.future.2015.11.015

Reference: FUTURE 2902

To appear in: Future Generation Computer Systems

Received date: 21 May 2015

Revised date: 4 November 2015

Accepted date: 10 November 2015

Please cite this article as: M.-A. Aufaure, R. Chiky, O. Cure´, H. Khrouf, G. Kepeklian, From

Business Intelligence to semantic data stream management, Future Generation Computer

Systems (2015), http://dx.doi.org/10.1016/j.future.2015.11.015

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From Business Intelligence to Semantic Data Stream

Management

Marie-Aude Aufaure1, Raja Chiky 2, Olivier Cure´3, Houda Khrouf 4, and Gabriel

Kepeklian4 1 MAS Lab Ecole Centrale Paris, France marie-aude.aufaure@ecp.fr 2 ISEP - LISITE, Paris, France raja.chiky@isep.fr 3 LIP6 (UMR 7606/CNRS)

Universite´ Pierre et Marie Curie (UPMC)

Paris France olivier.cure@lip6.fr 4 Atos Integration, Paris France firstname.lastname@atos.net

Abstract. The Semantic Web technologies are being increasingly used for exploiting relations between data. In addition, new tendencies of real-time systems, such as social networks, sensors, cameras or weather information, are continuously generating data. This implies that data and links between them are becoming extremely vast. Such huge quantity of data needs to be analyzed, processed, as well as stored if necessary. In this position paper, we will introduce recent work on Real-Time Business Intelligence combined with semantic data stream management. We will present underlying approaches such as continuous queries, data summarization and matching, and stream reasoning. 1 Introduction

The main objective of Business Intelligence is to transform data into knowledge for a better decision-making process. The constant growth of data and information, coming from heterogeneous data sources has lead to new ways of interaction and the integration of new models and tools to cope with this heterogeneity. We manipulate more and more unstructured data documents, emails, social networks, contacts that need to be integrated with classical structured data like CRM, data stored in relational databases.

We also need more and more interactivity, flexibility, dynamicity and expect the system to be proactive and reactive. Users expect immediate feedback, and want to find information rather than merely look for it. Moreover, the company tends to be organized in a collaborative way, called enterprise 2.0 [47]. All these evolutions induce challenging research topics for Business Intelligence, such as providing efficient mechanisms for a unified access and model to both structured and unstructured data. Semantic technologies are a perfect fit for integrating and matching data. Business Intelligence integrates collaborative and social software, by combining BI with elements from both Web 2.0 and the Semantic Web. Extracting value from all these data, a crucial advantage for *Manuscript

Click here to view linked References companies, requires business analytics. In order to synthesize information and derive insights from massive, dynamic, ambiguous data, the use of data visualization techniques and visual analytics becomes critical. Business Intelligence is also impacted by big data, and need to account for the volume of data sources as well as the need of response in real-time for extracting value from trusted data.

This position paper addresses the integration of real-time analytics with semantic technologies. Many research work has been done separately in these two fields, but, to the best of our knowledge, only a few ones provide an integrated view. This is mainly due to scalability issues for semantic reasoning.

The rest of this paper is organized as follows. Section 2 describes the new needs in Business Intelligence and present a generic architecture for semantic data stream management platform. Section 3 focuses on related work in the area of semantic data streaming. Sections 4 describes data matching in an RDF stream context. Section 5 gives an overview of reasoning in the context of RDF stream processing. Finally, Section 6 concludes this paper and gives an outlook upon future research for managing large-scale semantic streaming data. 2 From BI to Semantic data stream management

Business Intelligence (BI) refers to a set of tools and methods dedicated to collecting, representing and analyzing data to support decision-making in enterprises. BI is defined as the ability of an organization to take all input data and convert them into knowledge, ultimately, providing the right information to the right people at the right time via the right channel. During the last two decades, numerous tools have been designed to make available a huge amount of corporate data for non-expert users. Business Intelligence is a mature technology, widely adapted, but faces new challenges for incorporating new data such as unstructured data or data coming from sensors or social networks into analytics. A key issue is the ability to analyze in real-time these constantly growing amounts of data, taking their meaning into account. The complexity of BI tools and their interface is a barrier for their adoption. Thus, personalized systems and user modeling [31] ] have emerged to help provide more relevant information and services to the user.

Information visualization and dynamic interaction techniques are key for enhancing the user experience in using such tools[17].