Components Interoperability through Mediating Connector Patterns

A key objective for ubiquitous environments is to enable system interoperability between system's components that are highly heterogeneous. In particular, the challenge is to embed in the system architecture the necessary support to cope with behavioral diversity in order to allow components to coordinate and communicate. The continuously evolving environment further asks for an automated and on-the-fly approach. In this paper we present the design building blocks for the dynamic and on-the-fly interoperability between heterogeneous components. Specifically, we describe an Architectural Pattern called Mediating Connector, that is the key enabler for communication. In addition, we present a set of Basic Mediator Patterns, that describe the basic mismatches which can occur when components try to interact, and their corresponding solutions.Comment: In Proceedings WCSI 2010, arXiv:1010.233

Business intelligence project implementation : framework

Mestrado Bolonha em Data Analytics for BusinessIn recent years, businesses have experienced a substantial increase in data availability from which they recognize a source of value. As a result, business intelligence (BI) projects are in great demand as they offer a way to transform raw data into information that may assist decision-making and hence provide corporate value. This study provides a practical view of the implementation of a BI project into a business. Link Redglue is a company that helps clients get the most out of their data through an expertise approach. The internship described in this master’s final work integrates a BI project developed by Link Redglue, the purpose of which was to implement an existing BI solution for two new clients. The data that was to be analysed was related to these clients contact-centre processes. This report details, along with a theoretical background, the BI solution's implementation, from the contact centre’s source data to the Extract, Transform and Load (ETL) processes that brought data to the BI environment and were responsible for populating the already existing data warehouse, and finally, to the reporting layer which was responsible for representing the most important indicators related to the clients' contact-centre processes using Microsoft Excel. The client's BI solution implementation resulted in a data warehouse that provides a single view of all available data as well as reporting capabilities that can be used on a daily basis to assist decision-making regarding contact-centre operations of these clients.Nos últimos anos, um aumento substancial na disponibilidade de dados nas empresas tem vindo a ser notado, o qual é reconhecido por estas como uma fonte de valor. Consequentemente, os projetos de business intelligence (BI) têm vindo a ser cada vez mais procurados pelas empresas, dado que estes oferecem uma forma de transformar dados em informações relevantes que podem auxiliar na tomada de decisões e, consequentemente, gerar valor de negócio. O presente relatório apresenta uma visão prática de como foi implementado um projeto de BI num negócio. A empresa Link Redglue tem como objetivo ajudar os seus clientes a tirar o máximo valor dos seus dados. O estágio descrito neste trabalho final de mestrado foi parte de um projeto da Link Redglue que tinha como objetivo implementar uma solução de BI já existente para dois novos clientes. Os dados utilizados para esta implementação consistiam nos dados de processos de contact-centre destes dois clientes. Este relatório descreve, acompanhado por um enquadramento teórico, a implementação da solução de BI desde os dados operacionais de contact-centre do cliente, passando pelos processos de extração e transformação dos dados responsáveis por mover os dados até à data warehouse e finalmente ao processo de reporte dos resultados ao cliente utilizando Microsoft Excel para representar os indicadores mais relevantes nos seus processos de contact-centre. A implementação da solução de BI para o cliente resultou numa data warehouse que proporciona uma visão unificada de todos os dados disponíveis, bem como a disponibilização de relatórios que fornecem informação relevante diariamente para auxiliar na tomada de decisões.info:eu-repo/semantics/publishedVersio

Distantly Supervised Web Relation Extraction for Knowledge Base Population

Extracting information from Web pages for populating large, cross-domain knowledge bases requires methods which are suitable across domains, do not require manual effort to adapt to new domains, are able to deal with noise, and integrate information extracted from different Web pages. Recent approaches have used existing knowledge bases to learn to extract information with promising results, one of those approaches being distant supervision. Distant supervision is an unsupervised method which uses background information from the Linking Open Data cloud to automatically label sentences with relations to create training data for relation classifiers. In this paper we propose the use of distant supervision for relation extraction from the Web. Although the method is promising, existing approaches are still not suitable for Web extraction as they suffer from three main issues: data sparsity, noise and lexical ambiguity. Our approach reduces the impact of data sparsity by making entity recognition tools more robust across domains and extracting relations across sentence boundaries using unsupervised co- reference resolution methods. We reduce the noise caused by lexical ambiguity by employing statistical methods to strategically select training data. To combine information extracted from multiple sources for populating knowledge bases we present and evaluate several information integration strategies and show that those benefit immensely from additional relation mentions extracted using co-reference resolution, increasing precision by 8%. We further show that strategically selecting training data can increase precision by a further 3%

Developing an inter-enterprise alignment maturity model: research challenges and solutions

Business-IT alignment is pervasive today, as organizations strive to achieve competitive advantage. Like in other areas, e.g., software development, maintenance and IT services, there are maturity models to assess such alignment. Those models, however, do not specifically address the aspects needed for achieving alignment between business and IT in inter-enterprise settings. In this paper, we present the challenges we face in the development of an inter-enterprise alignment maturity model, as well as the current solutions to counter these problems

Entity reconciliation in big data sources: A systematic mapping study

The entity reconciliation (ER) problem aroused much interest as a research topic in today’s Big Dataera, full of big and open heterogeneous data sources. This problem poses when relevant information ona topic needs to be obtained using methods based on: (i) identifying records that represent the samereal world entity, and (ii) identifying those records that are similar but do not correspond to the samereal-world entity. ER is an operational intelligence process, whereby organizations can unify differentand heterogeneous data sources in order to relate possible matches of non-obvious entities. Besides, thecomplexity that the heterogeneity of data sources involves, the large number of records and differencesamong languages, for instance, must be added. This paper describes a Systematic Mapping Study (SMS) ofjournal articles, conferences and workshops published from 2010 to 2017 to solve the problem describedbefore, first trying to understand the state-of-the-art, and then identifying any gaps in current research.Eleven digital libraries were analyzed following a systematic, semiautomatic and rigorous process thathas resulted in 61 primary studies. They represent a great variety of intelligent proposals that aim tosolve ER. The conclusion obtained is that most of the research is based on the operational phase asopposed to the design phase, and most studies have been tested on real-world data sources, where a lotof them are heterogeneous, but just a few apply to industry. There is a clear trend in research techniquesbased on clustering/blocking and graphs, although the level of automation of the proposals is hardly evermentioned in the research work.Ministerio de Economía y Competitividad TIN2013-46928-C3-3-RMinisterio de Economía y Competitividad TIN2016-76956-C3-2-RMinisterio de Economía y Competitividad TIN2015-71938-RED

Dataset Discovery in Data Lakes

Data analytics stands to benefit from the increasing availability of datasets that are held without their conceptual relationships being explicitly known. When collected, these datasets form a data lake from which, by processes like data wrangling, specific target datasets can be constructed that enable value-adding analytics. Given the potential vastness of such data lakes, the issue arises of how to pull out of the lake those datasets that might contribute to wrangling out a given target. We refer to this as the problem of dataset discovery in data lakes and this paper contributes an effective and efficient solution to it. Our approach uses features of the values in a dataset to construct hash-based indexes that map those features into a uniform distance space. This makes it possible to define similarity distances between features and to take those distances as measurements of relatedness w.r.t. a target table. Given the latter (and exemplar tuples), our approach returns the most related tables in the lake. We provide a detailed description of the approach and report on empirical results for two forms of relatedness (unionability and joinability) comparing them with prior work, where pertinent, and showing significant improvements in all of precision, recall, target coverage, indexing and discovery times