Heterogeneous Information Sources Research Articles

Automated modelling assistance by integrating heterogeneous information sources

Model-Driven Engineering (MDE) uses models as its main assets in the software development process. The structure of a model is described through a meta-model. Even though modelling and meta-modelling are recurrent activities in MDE and a vast amount of MDE tools exist nowadays, they are tasks typically performed in an unassisted way. Usually, these tools cannot extract useful knowledge available in heterogeneous information sources like XML, RDF, CSV or other models and meta-models.We propose an approach to provide modelling and meta-modelling assistance. The approach gathers heterogeneous information sources in various technological spaces, and represents them uniformly in a common data model. This enables their uniform querying, by means of an extensible mechanism, which can make use of services, e.g., for synonym search and word sense analysis. The query results can then be easily incorporated into the (meta-)model being built. The approach has been realized in the Extremo tool, developed as an Eclipse plugin.Extremo has been validated in the context of two domains – production systems and process modelling – taking into account a large and complex industrial standard for classification and product description. Further validation results indicate that the integration of Extremo in various modelling environments can be achieved with low effort, and that the tool is able to handle information from most existing technological spaces.

Mapping Affinities in Academic Organizations

Scholarly affinities are one of the most fundamental hidden dynamics that drive scientific development. Some affinities are actual, and consequently can be measured through classical academic metrics such as co-authoring. Other affinities are potential, and therefore do not leave visible traces in information systems; for instance, some peers may share interests without actually knowing it. This article illustrates the development of a map of affinities for academic collectives, designed to be relevant to three audiences: the management, the scholars themselves, and the external public. Our case study involves the School of Architecture, Civil and Environmental Engineering of EPFL, hereinafter ENAC. The school consists of around one thousand scholars, seventy laboratories, and three institutes. The actual affinities are modeled using the data available from the information systems reporting publications, teaching, and advising scholars, whereas the potential affinities are addressed through text mining of the publications. The major challenge for designing such a map is to represent the multi-dimensionality and multi-scale nature of the information. The affinities are not limited to the computation of heterogeneous sources of information; they also apply at different scales. The map thus shows local affinities inside a given laboratory, as well as global affinities among laboratories. This article presents a graphical grammar to represent affinities. Its effectiveness is illustrated by two actualizations of the design proposal: an interactive online system in which the map can be parameterized, and a large-scale carpet of 250 square meters. In both cases, we discuss how the materiality influences the representation of data, in particular the way key questions could be appropriately addressed considering the three target audiences: the insights gained by the management and their consequences in terms of governance, the understanding of the scholars’ own positioning in the academic group in order to foster opportunities for new collaborations and, eventually, the interpretation of the structure from a general public to evaluate the relevance of the tool for external communication.

Particular Use of BIG DATA in Medical Diagnostic Tasks

The paper presents the main research results in the area of data mining application to medicine. We propose a new information technology of data mining for different classes of biomedical images based on the methodology of diagnostically relevant information selection and creation of informative characteristics. Application of Big Data technology in proposed systems of medical diagnostics has allowed to improve the learning set quality and reduce the classification error. Based on these results, the conclusion is made, that the usage of many heterogeneous sources of diagnostic information made it possible to improve the overall quality of the diagnostics.

An Ontology-Oriented Architecture for Dealing With Heterogeneous Data Applied to Telemedicine Systems

Current trends in medicine regarding issues of accessibility to and the quantity and quality of information and quality of service are very different compared to former decades. The current state requires new methods for addressing the challenge of dealing with enormous amounts of data present and growing on the Web and other heterogeneous data sources such as sensors and social networks and unstructured data, normally referred to as big data. Traditional approaches are not enough, at least on their own, although they were frequently used in hybrid architectures in the past. In this paper, we propose an architecture to process big data, including heterogeneous sources of information. We have defined an ontology-oriented architecture, where a core ontology has been used as a knowledge base and allows data integration of different heterogeneous sources. We have used natural language processing and artificial intelligence methods to process and mine data in the health sector to uncover the knowledge hidden in diverse data sources. Our approach has been applied to the field of personalized medicine (study, diagnosis, and treatment of diseases customized for each patient) and it has been used in a telemedicine system. A case study focused on diabetes is presented to prove the validity of the proposed model.

Le cause della crisi d’impresa. Analisi dei fattori di crisi delle grandi imprese in Amministrazione Straordinaria

Purpose of the paper: The aim of the paper is the evaluation of the main determinants of the crisis of large companies under extraordinary administration in order to verify if these factors are consistent with the explanatory models described in the literature. Methodology: The qualitative variables of the research are based on the systematization of the international literature. Two distinct research groups have evaluated the presence and relevance of each variable with respect to 86 corporate groups. The analytical comparison between the two groups has led to the the overall assessment. Findings: The crisis of the analyzed companies is primarily determined by endogenous strategic factors and imbalances in the capital structure. The study confirms that the main determinants of the crisis are consistent with the models of the business failure described in the economic literature. Research Limits: Additional empirical researches are required to assess the other variables of the business failure. In addition, the research analyses companies suited in the very advanced stages of the crisis. Practical Implications: The findings are the most important input for the implementation of tools to predict the evolution of the crisis. Originality of the paper: The research is based on an assessment model of the crisis’ determinants based on an analytical comparison of evaluations made on the basis of heterogeneous sources of information. In addition, the study confirms the validity of the theoretical constructs developed by the literature with regard to the analyzed large Italian companies.

A best-effort integration framework for imperfect information spaces

Entity resolution (ER) with imperfection management has been accepted as a major aspect while integrating heterogeneous information sources that exhibit entities with varied identifiers, abbreviated names, and multi-valued attributes. Many of novel integration applications such as personal information management and web-scale information management require the ability to represent and manipulate imperfect data. This requirement signifies the issues of starting with imperfect data to the production of probabilistic database. However, classical data integration (CDI) framework fails to cope with such requirement of explicit imperfect information management. This paper introduces an alternative integration framework based on the best-effort perspective to support instance integration automation. The new framework explicitly incorporates probabilistic management to the ER tasks. The probabilistic management includes a new probabilistic global entity, a new pair-wise-source-to-target ER process, and probabilistic decision model logic as alternatives. Together, the paper presents how these processes operate to support the current heterogeneous sources integration challenges.

ONTOLOGY MAPPING USING THE METHOD OF PARTICLE SWARM OPTIMIZATION

The paper is devoted to the integration of data from distributed heterogeneous information sources on the basis of ontology. The method of semantic similarity calculation between concepts allows to evaluate similarity between concepts. A technique for semantic similarity estimation between concepts of two ontologies is proposed. The main feature is the automatic calculation of weights using particle swarm optimization method. With the proposed approach, vectors of weights are generated. They correspond to different coefficients of semantic similarity. Then, using the obtained weights, the combination of different coefficients of semantic similarity is performed. Each concept from an ontology is attributed wit a set of relevant semantic concepts from another ontology. This allows to obtain sets of optimal and quasi-optimal solutions.

A best-effort integration framework for imperfect information spaces

Entity resolution (ER) with imperfection management has been accepted as a major aspect while integrating heterogeneous information sources that exhibit entities with varied identifiers, abbreviated names, and multi-valued attributes. Many of novel integration applications such as personal information management and web-scale information management require the ability to represent and manipulate imperfect data. This requirement signifies the issues of starting with imperfect data to the production of probabilistic database. However, classical data integration (CDI) framework fails to cope with such requirement of explicit imperfect information management. This paper introduces an alternative integration framework based on the best-effort perspective to support instance integration automation. The new framework explicitly incorporates probabilistic management to the ER tasks. The probabilistic management includes a new probabilistic global entity, a new pair-wise-source-to-target ER process, and probabilistic decision model logic as alternatives. Together, the paper presents how these processes operate to support the current heterogeneous sources integration challenges.

Information-rich surface metrology

Information-rich metrology refers to the incorporation of any type of available information in the data acquisition and processing pipeline of a measurement process, in order to improve the efficiency and quality of the measurement. In this work, the information-rich metrology paradigm is explored as it is applied to the measurement and characterisation of surface topography. The advantages and challenges of introducing heterogeneous information sources in the surface characterisation pipeline are illustrated. Examples are provided about the incorporation of structured knowledge about a part nominal geometry, the manufacturing processes with their signature topographic features and set-up parameters, and the measurement instruments with their performance characteristics and behaviour in relation to the specific properties of the surfaces being measured. A wide array of surface metrology applications, ranging from product inspection, to surface classification, to defect identification and to the investigation of advanced manufacturing processes, is used to illustrate the information-rich paradigm.

DAFNE: A Matlab toolbox for Bayesian multi-source remote sensing and ancillary data fusion, with application to flood mapping

High-resolution, remotely sensed images of the Earth surface have been proven to be of help in producing detailed flood maps, thanks to their synoptic overview of the flooded area and frequent revisits. However, flood scenarios can be complex situations, requiring the integration of different data in order to provide accurate and robust flood information. Several processing approaches have been recently proposed to efficiently combine and integrate heterogeneous information sources. In this paper, we introduce DAFNE, a Matlab®-based, open source toolbox, conceived to produce flood maps from remotely sensed and other ancillary information, through a data fusion approach. DAFNE is based on Bayesian Networks, and is composed of several independent modules, each one performing a different task. Multi-temporal and multi-sensor data can be easily handled, with the possibility of following the evolution of an event through multi-temporal output flood maps. Each DAFNE module can be easily modified or upgraded to meet different user needs. The DAFNE suite is presented together with an example of its application.

An Expert-driven Ontology-based Approach to the Collaborative Acquisition of Information

This article presents design decisions for an ontologybased framework supporting an expert-driven approach to the collaborative acquisition, integration and analysis of information. It describes a three-tier organization in which components facilitate establishing an information system to assist domain experts in addressing multi-causal dynamic situations where heterogeneous information sources must be integrated and the interaction of a variety of actors must be supported in large geographical areas with possibly low or intermittent connectivity levels. The approach and envisioned framework are illustrated with a case-study in the design of bikeways in urban zones.

Big Data sources and methods for social and economic analyses

The Data Big Bang that the development of the ICTs has raised is providing us with a stream of fresh and digitized data related to how people, companies and other organizations interact. To turn these data into knowledge about the underlying behavior of the social and economic agents, organizations and researchers must deal with such amount of unstructured and heterogeneous data. Succeeding in this task requires to carefully plan and organize the whole process of data analysis taking into account the particularities of the social and economic analyses, which include the wide variety of heterogeneous sources of information and a strict governance policy. Grounded on the data lifecycle approach, this paper develops a Big Data architecture that properly integrates most of the non-traditional information sources and data analysis methods in order to provide a specifically designed system for forecasting social and economic behaviors, trends and changes.

Global-patch-hybrid template-based arbitrary object tracking with integral channel features

Arbitrary object tracking is a challenging task in computer vision, as many factors affecting the target representation must be considered. A target template based on only the global appearance or on only the local appearance is unable to capture the discriminating information required for the robust performance of a tracker. In this paper, the target appearance is represented using a hybrid of global and local appearances along with a framework to exploit the Integral Channel Features (ICF). The proposed hybrid approach achieves fusion of the conventional global and patch-based approaches for target representation to synergize the advantages of both approaches. The ICF approach under the hybrid approach integrates heterogeneous sources of information of the target and provides feature strength to the hybrid template. The use of ICF also expedites the extraction of the structural and color features from video frames as the features are collected over multiple channels. The target appearance representation is updated based on only samples with appearances similar to the target appearance using clustering and vector quantization. These factors offer the proposed algorithm robustness to occlusion, illumination changes, and in-plane rotation. Further experimentation analyzes the effects of a change in the scale of the bounding box on the tracking performance of the proposed algorithm. The proposed approach outperforms all the state-of-the-art algorithms in all considered scenarios.

Establishing interoperability among knowledge organization systems for research management: a social network approach

Knowledge organization systems (KOS) have long been established as a tool to represent organized interpretation of knowledge structures. Various types of KOS such as discipline tree for research projects, subject categories for research publications, and classifications schemes for research patents have been constructed and widely used in R&D contexts. However, the incompatible KOS, together with information proliferation in the Big Data Era, impose great challenges for effective research management. How to facilitate interoperability among heterogeneous research information sources is an important problem to be solved. KOS mapping methods were proposed to provide alternative subject access by establishing equivalence or partial equivalence relationships between classes in different KOS but suffered from “lagging mapping” and “deficient mapping”. This research proposes a social network approach that leverages online social platform information (i.e., research activities and social activities) for KOS mapping. The underlying assumption behind the approach is that “two classes/terms in different KOS are related if their corresponding research objects are connected to similar researchers”. The social network approach leverages social network analysis instead of semantic and structure analysis of metadata information for mapping degree calculation. The approach has been implemented on the largest research social platform in China and successfully realizes mapping between KOS for research management purpose. We conducted mapping between National Natural Science Foundation of China discipline tree and Web of Science subject categories in this study to examine the performance of social network approach.

Multimedia retrieval based on non-linear graph-based fusion and partial least squares regression

Heterogeneous sources of information, such as images, videos, text and metadata are often used to describe different or complementary views of the same multimedia object, especially in the online news domain and in large annotated image collections. The retrieval of multimedia objects, given a multimodal query, requires the combination of several sources of information in an efficient and scalable way. Towards this direction, we provide a novel unsupervised framework for multimodal fusion of visual and textual similarities, which are based on visual features, visual concepts and textual metadata, integrating non-linear graph-based fusion and Partial Least Squares Regression. The fusion strategy is based on the construction of a multimodal contextual similarity matrix and the non-linear combination of relevance scores from query-based similarity vectors. Our framework can employ more than two modalities and high-level information, without increase in memory complexity, when compared to state-of-the-art baseline methods. The experimental comparison is done in three public multimedia collections in the multimedia retrieval task. The results have shown that the proposed method outperforms the baseline methods, in terms of Mean Average Precision and Precision@20.

Listeriomics: an Interactive Web Platform for Systems Biology of Listeria.

As for many model organisms, the amount of Listeria omics data produced has recently increased exponentially. There are now >80 published complete Listeria genomes, around 350 different transcriptomic data sets, and 25 proteomic data sets available. The analysis of these data sets through a systems biology approach and the generation of tools for biologists to browse these various data are a challenge for bioinformaticians. We have developed a web-based platform, named Listeriomics, that integrates different tools for omics data analyses, i.e., (i) an interactive genome viewer to display gene expression arrays, tiling arrays, and sequencing data sets along with proteomics and genomics data sets; (ii) an expression and protein atlas that connects every gene, small RNA, antisense RNA, or protein with the most relevant omics data; (iii) a specific tool for exploring protein conservation through the Listeria phylogenomic tree; and (iv) a coexpression network tool for the discovery of potential new regulations. Our platform integrates all the complete Listeria species genomes, transcriptomes, and proteomes published to date. This website allows navigation among all these data sets with enriched metadata in a user-friendly format and can be used as a central database for systems biology analysis. IMPORTANCE In the last decades, Listeria has become a key model organism for the study of host-pathogen interactions, noncoding RNA regulation, and bacterial adaptation to stress. To study these mechanisms, several genomics, transcriptomics, and proteomics data sets have been produced. We have developed Listeriomics, an interactive web platform to browse and correlate these heterogeneous sources of information. Our website will allow listeriologists and microbiologists to decipher key regulation mechanism by using a systems biology approach.

BIG DATA – NEW APPROACH TO BUSINESS KNOWLEDGE CREATION

The article is devoted to the topic of creation of business knowledge with usage of new heterogeneous information sources and specific technologies, united under the concept – “big data”. It was analyzed various definitions of the “Big data” term as well as specifics of that particular technology. It was formulated some key differentiations of “Big data” from traditional technologies. Based on that analysis it was made a conclusion regarding the value of this technology for gaining a competitive advantage.

An Ontology-Based Approach for Marine Geochemical Data Interoperation

International deep seabed resource surveys have accumulated a large amount of valuable marine geochemical data. These data, however, derive from a number of autonomous heterogeneous information sources that show the characteristics of big data, i.e., multidisciplinary, multidimensional, multisemantic, and with strong correlations. The traditional database federation method is thus not applicable. To achieve marine geochemical data interoperation, an approach is proposed in this paper. The approach first builds a marine sample ontology (MSO) based on marine geochemical metadata standards. With support of the MSO ontology, which serves as a specification for the semantics and model of multisource heterogeneous data, the data integration and unified query APIs are accomplished across different databases. The experiment is applied with three databases: the ICP-AES database model in the ODP project, the PetDB marine petrology database model compiled at the Lamont–Doherty Earth Observatory, and the GEOROC database model operated by the German Max Planck Institute. The experimental results show that this method not only improves the efficiency of the marine geochemical data integration but also realizes the reuse of data and models under the premise of ensuring the independence, security, and timeliness of the data sources.

Towards an Obesity-Cancer Knowledge Base: Biomedical Entity Identification and Relation Detection.

Obesity is associated with increased risks of various types of cancer, as well as a wide range of other chronic diseases. On the other hand, access to health information activates patient participation, and improve their health outcomes. However, existing online information on obesity and its relationship to cancer is heterogeneous ranging from pre-clinical models and case studies to mere hypothesis-based scientific arguments. A formal knowledge representation (i.e., a semantic knowledge base) would help better organizing and delivering quality health information related to obesity and cancer that consumers need. Nevertheless, current ontologies describing obesity, cancer and related entities are not designed to guide automatic knowledge base construction from heterogeneous information sources. Thus, in this paper, we present methods for named-entity recognition (NER) to extract biomedical entities from scholarly articles and for detecting if two biomedical entities are related, with the long term goal of building a obesity-cancer knowledge base. We leverage both linguistic and statistical approaches in the NER task, which supersedes the state-of-the-art results. Further, based on statistical features extracted from the sentences, our method for relation detection obtains an accuracy of 99.3% and a f-measure of 0.993.

Heterogeneous Information Knowledge Construction Based on Ontology

Describing and representing multi-source and heterogeneous knowledge is a popular research topic in recent years. After investigating knowledge forming process based on multi-source heterogeneous information resources, we present a new approach in which different information resources are put into a mutual RDF(S) data model, and semantic reasoning of RDF(S) is conducted. Moreover, a knowledge base construction framework for multi-source heterogeneous information source with combination of Ontology knowledge model is put forward, and an algorithm of knowledge base construction is also proposed, in which the core issues are knowledge inclusion and updating. Then the time complexity of our algorithm is analyzed. Finally, in order to solve the heterogeneous, and uneven horizontal of geographical distribution of ethnic minority information resources in Yunnan Province, we use the proposed method to construct a domain knowledge base for ethnic minority information resources, and use this model to evaluate the efficiency for the knowledge inclusion algorithm in responding time and indexing responding time for different data resources in our experiments.