Workflows and service discovery: a mobile device approach

Bioinformatics has moved from command-line standalone programs to web-service based environments. Such trend has resulted in an enormous amount of online resources which can be hard to find and identify, let alone execute and exploit. Furthermore, these resources are aimed -in general- to solve specific tasks. Usually, this tasks need to be combined in order to achieve the desired results. In this line, finding the appropriate set of tools to build up a workflow to solve a problem with the services available in a repository is itself a complex exercise. Issues such as services discovering, composition and representation appear. On the technological side, mobile devices have experienced an incredible growth in the number of users and technical capabilities. Starting from this reality, in the present paper, we propose a solution for service discovering and workflow generation while distinct approaches of representing workflows in a mobile environment are reviewed and discussed. As a proof of concept, a specific use case has been developed: we have embedded an expanded version of our Magallanes search engine into mORCA, our mobile client for bioinformatics. Such composition delivers a powerful and ubiquitous solution that provides the user with a handy tool for not only generate and represent workflows, but also services, data types, operations and service types discoveryUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Technology Selection for Big Data and Analytical Applications

The term Big Data has become pervasive in recent years, as smart phones, televisions, washing machines, refrigerators, smart meters, diverse sensors, eyeglasses, and even clothes connect to the Internet. However, their generated data is essentially worthless without appropriate data analytics that utilizes information retrieval, statistics, as well as various other techniques. As Big Data is commonly too big for a single person or institution to investigate, appropriate tools are being used that go way beyond a traditional data warehouse and that have been developed in recent years. Unfortunately, there is no single solution but a large variety of different tools, each of which with distinct functionalities, properties and characteristics. Especially small and medium-sized companies have a hard time to keep track, as this requires time, skills, money, and specific knowledge that, in combination, result in high entrance barriers for Big Data utilization. This paper aims to reduce these barriers by explaining and structuring different classes of technologies and the basic criteria for proper technology selection. It proposes a framework that guides especially small and mid-sized companies through a suitable selection process that can serve as a basis for further advances

Free Open Source Software for Business Intelligence

Free Open Source Software (FOSS) has recently grown, becoming a significant part of the IT market. We use the word “FOSS” to refer to software under a license which grants the right to access the source code and use, study, and change the software. We must not confuse FOSS with “non-commercial software”: antonyms of FOSS are “closed” and “proprietary” software. The first purpose of this paper is to maintain an unbiased position. The analysis begins with a general overview of the FOSS world and then moves focus to business intelligence: during the last years, several tools have finally entered the market, becoming actual competitors to proprietary software. Although FOSS still needs to grow, a large number of companies are already deploying or at least testing some FOSS solutions. In addition, the research world has shown interest providing several market surveys and software analyses. After illustrating the selection criteria used, the paper describes the most interesting FOSS tools for each of the following business intelligence subcategories: database management systems (DBMS), data integration tools, analytical tools and business intelligence suites. In addition, the FOSS data mining solutions RapidMiner and KNIME are evaluated and tested on a set of data. Although the two programs are not as widespread as the proprietary data mining tools, they can be considered actual competitors to the proprietary software

Management Information System - Human Resource & Payroll System for Sri Lanka Ports Authority

Payroll is the one of most critical process in every large organization; it has many functionalities to fulfill on time for a smooth payroll process. When the number of employees varies on time to time due to retirements and new appointments and calculation method differs based on it, increases the complication of the system. Management operations concerning data can be a complicated scenario when the amount of data involved is large. More importantly, when the data involved requires a high level of accuracy, then the management role becomes hectic. Payroll is a sector of the management in which accuracy is required and a lapse in the data can lead to adverse effect on employee motivation and production. In such system, cost estimation is very vital for top management proceed with future organizational plans

QoS oriented MapReduce Optimization for Hadoop Based BigData Application

International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc

A framework for supporting knowledge representation – an ontological based approach

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de ComputadoresThe World Wide Web has had a tremendous impact on society and business in just a few years by making information instantly available. During this transition from physical to electronic means for information transport, the content and encoding of information has remained natural language and is only identified by its URL. Today, this is perhaps the most significant obstacle to streamlining business processes via the web. In order that processes may execute without human intervention, knowledge sources, such as documents, must become more machine understandable and must contain other information besides their main contents and URLs. The Semantic Web is a vision of a future web of machine-understandable data. On a machine understandable web, it will be possible for programs to easily determine what knowledge sources are about. This work introduces a conceptual framework and its implementation to support the classification and discovery of knowledge sources, supported by the above vision, where such sources’ information is structured and represented through a mathematical vector that semantically pinpoints the relevance of those knowledge sources within the domain of interest of each user. The presented work also addresses the enrichment of such knowledge representations, using the statistical relevance of keywords based on the classical vector space model concept, and extending it with ontological support, by using concepts and semantic relations, contained in a domain-specific ontology, to enrich knowledge sources’ semantic vectors. Semantic vectors are compared against each other, in order to obtain the similarity between them, and better support end users with knowledge source retrieval capabilities

Semantic enrichment of knowledge sources supported by domain ontologies

This thesis introduces a novel conceptual framework to support the creation of knowledge representations based on enriched Semantic Vectors, using the classical vector space model approach extended with ontological support. One of the primary research challenges addressed here relates to the process of formalization and representation of document contents, where most existing approaches are limited and only take into account the explicit, word-based information in the document. This research explores how traditional knowledge representations can be enriched through incorporation of implicit information derived from the complex relationships (semantic associations) modelled by domain ontologies with the addition of information presented in documents. The relevant achievements pursued by this thesis are the following: (i) conceptualization of a model that enables the semantic enrichment of knowledge sources supported by domain experts; (ii) development of a method for extending the traditional vector space, using domain ontologies; (iii) development of a method to support ontology learning, based on the discovery of new ontological relations expressed in non-structured information sources; (iv) development of a process to evaluate the semantic enrichment; (v) implementation of a proof-of-concept, named SENSE (Semantic Enrichment kNowledge SourcEs), which enables to validate the ideas established under the scope of this thesis; (vi) publication of several scientific articles and the support to 4 master dissertations carried out by the department of Electrical and Computer Engineering from FCT/UNL. It is worth mentioning that the work developed under the semantic referential covered by this thesis has reused relevant achievements within the scope of research European projects, in order to address approaches which are considered scientifically sound and coherent and avoid “reinventing the wheel”.European research projects - CoSpaces (IST-5-034245), CRESCENDO (FP7-234344) and MobiS (FP7-318452

Social media competitive analysis and text mining: a case study in digital marketing in the hospitality industry

Objectives The main objectives of this study were to explore the effectiveness of using text mining to analyse the consumer generated content from online hotel reviews. Specifically, this study focuses on demonstrating the helpfulness of such tools in the case of Original Sokos Hotel Vaakuna Helsinki and Scandic Marski in Finland. By analyzing the current trends and patterns of the online reviews of the two hotels, the objective of the study is to understand the extent to which text mining can improve marketing decisions and thus bring value to consumers. Summary The tourism and hospitality industry has changed tremendously due to the emergence of online review platforms such as TripAdvisor.com. This study applies text mining analytics to conduct a content analysis on the social media content provided by hotel guests on these platforms. To gain competitive insights from the data, topic classification and sentiment analysis are used. Conclusions The findings of the research illustrate how topics and related sentiment can be identified from the online content. Although there are several similarities between the data regarding online discussion, the text mining analysis also identified some differences, which have the potential to contribute to gaining competitive intelligence in the industry. Overall, the study illustrates how simple text mining software, which requires little resources from firms can provide beneficial information about the market to hotels in international business