Data Warehouse (DW) and OLAP systems are first citizens of Business Intelligence tools. They are widely used in the academic and industrial communities for numerous different fields of application. Despite the maturity of DW and OLAP systems, with the advent of Big Data, more and more sources of data are available, and warehousing this data can lead to important quality issues. In this work, we focus

Personalised analytics is a powerful technology that can be used to improve the career, lifestyle, and health of individuals by providing them with an in-depth analysis of their characteristics as compared to other people. Existing research has often focused on mining general patterns or clusters, but without the facility for customisation to an individual’s needs. It is challenging to adapt such approaches

Argument mining consists in the automatic identification of argumentative structures in texts. In this work we leverage existing discourse-level annotations to facilitate the identification of argumentative components and relations in scientific texts, which has been recognized as a particularly challenging task. We propose a new annotation schema and use it to augment a corpus of computational linguistics

Extraction, transformation, and loading (ETL) processes are used to extract data from internal and external sources of an organization, transform these data, and load them into a data warehouse. The Business Process Modeling and Notation (BPMN) has been proposed for expressing ETL processes at a conceptual level. A different approach is studied in this paper, where relational algebra (RA), extended

In this paper we propose an innovative architecture, called Mo.Re.Farming, for handling agricultural data in an integrated fashion and supporting decision making in the precision agriculture domain. This architecture is oriented to data analysis and is inspired by Business Intelligence 2.0 approaches. It is hybrid in that it couples traditional and big data technologies to integrate heterogeneous data

Discovery, specification and proper representation of various aspects of business knowledge plays crucial part in model-driven information systems engineering, especially when it comes to the early stages of systems development. Being among the most applicable and advanced features of model-driven development, model transformation could help improving one of the most time- and resource-consuming efforts

Searching the query log of a database system has a variety of applications. In a complex database, relevant queries in the log can serve as an initial example for query formulation, or may elucidate how to query the data in an optimized manner. Searching for queries that may cause a security or a privacy breach could be used to detect leaks of sensitive data. In general, queries in the query log can

Privacy-Preserving Record Linkage (PPRL) supports the integration of sensitive information from multiple datasets, in particular the privacy-preserving matching of records referring to the same entity. PPRL has gained much attention in many application areas, with the most prominent ones in the healthcare domain. PPRL techniques tackle this problem by conducting linkage on masked (encoded) values.

Conversational recommender system is a tool to help customer in deciding products they are going to buy, by conversational mechanism. By this mechanism, the system is able to imitate natural conversation between customer and professional sales support, for eliciting customer preference. However, many customers are not familiar with the technical features of multi-function and multi-feature products

Lower-dimensional representation of scientific text has attracted much attention among researchers due to its impact on many data mining and recommendation tasks. This paper studies two main research streams in scientific literature representation. First, both local and distributed representation viewpoints are reviewed and their advantages and disadvantages in lower dimensional representation are

There are numerous applications where users wish to identify subsets of vertices in a social network or graph database that are of interest to them. They may specify sets of patterns and vertex properties, and each of these confers a score to a subgraph. The users want to find the subgraphs with top-k highest scores. Examples in the real world where such subgraphs involve custom scoring methods include:

Skyline query has attracted a great deal of interest during last years because of its ability to help decision makers when multi-criteria objectives are to be handled. Several authors have pointed the interest of multidimensional skylines, i.e., the set of criteria become a parameter of the query. In order to efficiently evaluate these queries, index structures have been proposed. In this paper, we

The process of converting natural language specifications into conceptual models requires detailed analysis of natural language text, and designers frequently make mistakes when undertaking this transformation manually. Although many approaches have been used to partly automate this process, one of the main limitations is the lack of a domain-independent ontology that can be used as a repository for

Text similarity measures play a very important role in several text mining applications. Although there is an extensive literature on measuring the similarity between long texts, there is less work related to the measurement of similarity between short texts. And most of these works on short text similarity are based on adaptations of long-text similarity methods. Unfortunately, the description of

Heterogeneous information networks (HINs) composed of multiple types of nodes and links, play increasingly important roles in real life applications. Classification of the related data is an essential work in network analysis. Existing methods can effectively solve these classification tasks when they are applied to homogeneous information networks and simple data, but not for the noisy and sparse

Big data is progressively being used in various areas, such as industry, financial dealing, medicine, and so on, as it can handle the challenges in processing large amounts of data. One of the data mining techniques used widely and effectively to classify big data is the MapReduce model. In this paper, an approach for the classification of big data is developed using Cuckoo–Grey wolf based Correlative

Trained indexers at the National Library of Medicine (NLM) manually tag each biomedical abstract with the most suitable terms from the Medical Subject Headings (MeSH) terminology to be indexed by their PubMed information system. MeSH has over 26,000 terms and indexers look at each article's full text while assigning the terms. Recent automated attempts focused on using the article title and abstract

Web-based collaboration and virtual environments supported by various Web 2.0 concepts enable the application of numerous monitoring, mining and analysis tools to study human interactions and team formation processes. The composition of an effective team requires a balance between adequate skill fulfillment and sufficient team connectivity. The underlying interaction structure reflects social behavior

Identifying time periods with a burst of activities related to a topic has been an important problem in analyzing time-stamped documents. In this paper, we propose an approach to extract a hot spot of a given topic in a time-stamped document set. Topics can be basic, containing a simple list of keywords, or complex. Logical relationships such as and, or, and not are used to build complex topics from