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Authors: Lyes Limam ; Jean Lecouffe and Stéphane Chau

Affiliation: Altran Research, France

ISBN: 978-989-758-187-8

Keyword(s): Big Data, Business Intelligence, Text Mining, Graph Databases.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Cloud Computing ; Data Engineering ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Health Information Systems ; Information Systems Analysis and Specification ; Knowledge Management ; Non-Relational Databases ; Ontologies and the Semantic Web ; Semantic Web Technologies ; Sensor Networks ; Services Science ; Signal Processing ; Society, e-Business and e-Government ; Soft Computing ; Software Agents and Internet Computing ; Web Information Systems and Technologies

Abstract: Modern enterprise information systems are characterized by large amounts of data issued from various internal and external business applications, often stored and archived in different supports (databases, documents, etc. The nature of this data (voluminous, unstructured, heterogeneous, inconsistent, etc.) makes them difficult to use for analysis. In fact, it is typically an issue of big data analytics. The main objective of our research project is to design a solution to detect opportunities (projects, new markets, skills, tenders, etc.) in the continually growing data, while adapting to its constraints. The extracted information should help users to take proactive actions to improve their business (e.g., identify a consultant skill that can be aligned with a given tender). In this project we are interested in text data. There are two main reasons. The first reason is that text data is the most difficult to analyse by humans, especially when it is voluminous. The second reason is tha t we are convinced that valuable information is usually textual. Therefore, we define six research axes: • Intelligent Information Sensing • Text Mining • Knowledge Representation (semantics) • Querying the knowledge • Results Interpretation • Self-learning. (More)

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Paper citation in several formats:
Limam, L.; Lecouffe, J. and Chau, S. (2016). Business Opportunity Detection in the Big Data.In Proceedings of the 18th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-187-8, pages 250-254. DOI: 10.5220/0005917402500254

@conference{iceis16,
author={Lyes Limam. and Jean Lecouffe. and Stéphane Chau.},
title={Business Opportunity Detection in the Big Data},
booktitle={Proceedings of the 18th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2016},
pages={250-254},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005917402500254},
isbn={978-989-758-187-8},
}

TY - CONF

JO - Proceedings of the 18th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Business Opportunity Detection in the Big Data
SN - 978-989-758-187-8
AU - Limam, L.
AU - Lecouffe, J.
AU - Chau, S.
PY - 2016
SP - 250
EP - 254
DO - 10.5220/0005917402500254

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