Data Format

Collaborative tagging has emerged as a common solution for labelling and organising online digital content. However, collaborative tagging systems typically suffer from a number of issues such as tag scarcity or ambiguous labelling. As a result, the organisation and browsing of tagged content is far from being optimal. In this work the authors present a general scheme for building a folksonomy-based tag recommendation system to help users tagging online content resources. Based on this general scheme, the authorse describe eight tag recommendation methods and extensively evaluate them with data coming from two real-world large-scale datasets of tagged images and sound clips. Their results show that the proposed methods can effectively recommend relevant tags, given a set of input tags and tag co-occurrence information. Moreover, the authors show how novel strategies for selecting the appropriate number of tags to be recommended can significantly improve methods performances. Approac...

Conceptual models are used in understanding and communicating the domain of interest during analysis phase of system development. As they are used in early phases, errors and omissions may propagate to later phases and may be very costly to correct. This paper proposes a framework for evaluating conceptual models when represented in a domain specific language based on UML constructs.

Both conceptual modeling and machine learning have long been recognized as important areas of research. With the increasing emphasis on digitizing and processing large amounts of data for business and other applications, it would be helpful to consider how these areas of research can complement each other. To understand how they can be paired, we provide an overview of machine learning foundations and development cycle. We then examine how conceptual modeling can be applied to machine learning and propose a framework for incorporating conceptual modeling into data science projects. The framework is illustrated by applying it to a healthcare application. For the inverse pairing, machine learning can impact conceptual modeling through text and rule mining, as well as knowledge graphs. The pairing of conceptual modeling and machine learning in this this way should help lay the foundations for future research.

Named entity recognition (NER) is one of the fundamental tasks in natural-language processing (NLP). Though the combination of different classifiers has been widely applied in several well-studied languages, this is the first time this method has been applied to Vietnamese. In this article, we describe how voting techniques can improve the performance of Vietnamese NER. By combining several state-of-the-art machine-learning algorithms using voting strategies, our final result outperforms individual algorithms and gained an F-measure of 89.12. A detailed discussion about the challenges of NER in Vietnamese is also presented.

With increasingly higher numbers of non-English language web searchers the problems of efficient handling of non-English Web documents and user queries are becoming major issues for search engines. The main aim of this review paper is to make researchers aware of the existing problems in monolingual non-English Web retrieval by providing an overview of open issues. A significant number of

The data warehouse design task needs to consider both the end-user requirements and the organization data sources. For this reason, the data warehouse design has been traditionally considered a reengineering process, guided by requirements, from the data sources.Most current design methods available demand highly-expressive end-user requirements as input, in order to carry out the exploration and analysis of the data

This experience paper summarizes the key lessons we learned throughout the design and implementation of the Aurora stream-processing engine. For the past 2 years, we have built five stream-based applications using Aurora. We first describe in detail these applications and their implementation in Aurora. We then reflect on the design of Aurora based on this experience. Finally, we discuss our initial ideas on a follow-on project, called Borealis, whose goal is to eliminate the limitations of Aurora as well as to address new key ...

When NoSQL database systems are used in an agile software development setting, data model changes occur frequently and thus, data is routinely stored in different versions. The management of versioned data leads to an overhead potentially impeding the software development. Several data migration strategies exist that handle legacy data differently during data accesses, each of which can be characterized by certain advantages and disadvantages. Depending on the requirements for the software application, we evaluate and compare different migration strategies through metrics like migration costs and latency as well as precision and recall. Ideally, exactly that strategy should be selected whose characteristics fulfill service-level agreements and match the migration scenario, which depends on the query workload and the changes in the data model which imply an evolution of the database schema. In this paper, we present a methodology of self-adapting data migration, which automatically a...

Knowledge management is a critical issue in today's business world. Knowledge is considered to be one of the most strategic resources of a firm and sources of competitive advantage. This paper identifies and ranks Critical Success Factors (CSFs) for implementation of knowledge management in the Iranian Energy Sector. Using the Analytic hierarchy process (AHP) method the relative quantitative weights of 8 of the major CSFs for implementation of knowledge management are identified, based on analyses with KM designers in the Iranian energy sector. The outcomes of the research designate that the critical success factors in the order of importance are: corporate culture, human and financial resources, strategy and leadership, structures and procedures, meso environmental factors, knowledge management process, macro environmental factors, technology and infrastructure.

This paper presents a generic approach to integrate environmental sensor data efficiently, allowing the detection of relevant situations and events in near real-time through continuous querying. Data variety is addressed with the use of the Semantic Sensor Network ontology for observation data modelling, and semantic annotations for environmental phenomena. Data velocity is handled by distributing sensor data messaging and serving observations as RDF graphs on query demand. The stream processing engine presented in the paper, morph-streams++, provides adapters for different data formats and distributed processing of streams in a cluster. An evaluation of different configurations for parallelization and semantic annotation parameters proves that the described approach reduces the average latency of message processing in some cases.