XML Matchers: approaches and challenges

Schema Matching, i.e. the process of discovering semantic correspondences between concepts adopted in different data source schemas, has been a key topic in Database and Artificial Intelligence research areas for many years. In the past, it was largely investigated especially for classical database models (e.g., E/R schemas, relational databases, etc.). However, in the latest years, the widespread adoption of XML in the most disparate application fields pushed a growing number of researchers to design XML-specific Schema Matching approaches, called XML Matchers, aiming at finding semantic matchings between concepts defined in DTDs and XSDs. XML Matchers do not just take well-known techniques originally designed for other data models and apply them on DTDs/XSDs, but they exploit specific XML features (e.g., the hierarchical structure of a DTD/XSD) to improve the performance of the Schema Matching process. The design of XML Matchers is currently a well-established research area. The main goal of this paper is to provide a detailed description and classification of XML Matchers. We first describe to what extent the specificities of DTDs/XSDs impact on the Schema Matching task. Then we introduce a template, called XML Matcher Template, that describes the main components of an XML Matcher, their role and behavior. We illustrate how each of these components has been implemented in some popular XML Matchers. We consider our XML Matcher Template as the baseline for objectively comparing approaches that, at first glance, might appear as unrelated. The introduction of this template can be useful in the design of future XML Matchers. Finally, we analyze commercial tools implementing XML Matchers and introduce two challenging issues strictly related to this topic, namely XML source clustering and uncertainty management in XML Matchers.Comment: 34 pages, 8 tables, 7 figure

Semantic Similarity of Spatial Scenes

The formalization of similarity in spatial information systems can unleash their functionality and contribute technology not only useful, but also desirable by broad groups of users. As a paradigm for information retrieval, similarity supersedes tedious querying techniques and unveils novel ways for user-system interaction by naturally supporting modalities such as speech and sketching. As a tool within the scope of a broader objective, it can facilitate such diverse tasks as data integration, landmark determination, and prediction making. This potential motivated the development of several similarity models within the geospatial and computer science communities. Despite the merit of these studies, their cognitive plausibility can be limited due to neglect of well-established psychological principles about properties and behaviors of similarity. Moreover, such approaches are typically guided by experience, intuition, and observation, thereby often relying on more narrow perspectives or restrictive assumptions that produce inflexible and incompatible measures. This thesis consolidates such fragmentary efforts and integrates them along with novel formalisms into a scalable, comprehensive, and cognitively-sensitive framework for similarity queries in spatial information systems. Three conceptually different similarity queries at the levels of attributes, objects, and scenes are distinguished. An analysis of the relationship between similarity and change provides a unifying basis for the approach and a theoretical foundation for measures satisfying important similarity properties such as asymmetry and context dependence. The classification of attributes into categories with common structural and cognitive characteristics drives the implementation of a small core of generic functions, able to perform any type of attribute value assessment. Appropriate techniques combine such atomic assessments to compute similarities at the object level and to handle more complex inquiries with multiple constraints. These techniques, along with a solid graph-theoretical methodology adapted to the particularities of the geospatial domain, provide the foundation for reasoning about scene similarity queries. Provisions are made so that all methods comply with major psychological findings about people’s perceptions of similarity. An experimental evaluation supplies the main result of this thesis, which separates psychological findings with a major impact on the results from those that can be safely incorporated into the framework through computationally simpler alternatives

Business Intelligence on Non-Conventional Data

The revolution in digital communications witnessed over the last decade had a significant impact on the world of Business Intelligence (BI). In the big data era, the amount and diversity of data that can be collected and analyzed for the decision-making process transcends the restricted and structured set of internal data that BI systems are conventionally limited to. This thesis investigates the unique challenges imposed by three specific categories of non-conventional data: social data, linked data and schemaless data. Social data comprises the user-generated contents published through websites and social media, which can provide a fresh and timely perception about people’s tastes and opinions. In Social BI (SBI), the analysis focuses on topics, meant as specific concepts of interest within the subject area. In this context, this thesis proposes meta-star, an alternative strategy to the traditional star-schema for modeling hierarchies of topics to enable OLAP analyses. The thesis also presents an architectural framework of a real SBI project and a cross-disciplinary benchmark for SBI. Linked data employ the Resource Description Framework (RDF) to provide a public network of interlinked, structured, cross-domain knowledge. In this context, this thesis proposes an interactive and collaborative approach to build aggregation hierarchies from linked data. Schemaless data refers to the storage of data in NoSQL databases that do not force a predefined schema, but let database instances embed their own local schemata. In this context, this thesis proposes an approach to determine the schema profile of a document-based database; the goal is to facilitate users in a schema-on-read analysis process by understanding the rules that drove the usage of the different schemata. A final and complementary contribution of this thesis is an innovative technique in the field of recommendation systems to overcome user disorientation in the analysis of a large and heterogeneous wealth of data

Drug-drug interactions: A machine learning approach

Automatic detection of drug-drug interaction (DDI) is a difficult problem in pharmaco-surveillance. Recent practice for in vitro and in vivo pharmacokinetic drug-drug interaction studies have been based on carefully selected drug characteristics such as their pharmacological effects, and on drug-target networks, in order to identify and comprehend anomalies in a drug\u27s biochemical function upon co-administration.;In this work, we present a novel DDI prediction framework that combines several drug-attribute similarity measures to construct a feature space from which we train three machine learning algorithms: Support Vector Machine (SVM), J48 Decision Tree and K-Nearest Neighbor (KNN) using a partially supervised classification algorithm called Positive Unlabeled Learning (PU-Learning) tailored specifically to suit our framework.;In summary, we extracted 1,300 U.S. Food and Drug Administration-approved pharmaceutical drugs and paired them to create 1,688,700 feature vectors. Out of 397 drug-pairs known to interact prior to our experiments, our system was able to correctly identify 80% of them and from the remaining 1,688,303 pairs for which no interaction had been determined, we were able to predict 181 potential DDIs with confidence levels greater than 97%. The latter is a set of DDIs unrecognized by our source of ground truth at the time of study.;Evaluation of the effectiveness of our system involved querying the U.S. Food and Drug Administration\u27s Adverse Effect Reporting System (AERS) database for cases involving drug-pairs used in this study. The results returned from the query listed incidents reported for a number of patients, some of whom had experienced severe adverse reactions leading to outcomes such as prolonged hospitalization, diminished medicinal effect of one or more drugs, and in some cases, death

Semantic Biclustering

Tato disertační práce se zaměřuje na problém hledání interpretovatelných a prediktivních vzorů, které jsou vyjádřeny formou dvojshluků, se specializací na biologická data. Prezentované metody jsou souhrnně označovány jako sémantické dvojshlukování, jedná se o podobor dolování dat. Termín sémantické dvojshlukování je použit z toho důvodu, že zohledňuje proces hledání koherentních podmnožin řádků a sloupců, tedy dvojshluků, v 2-dimensionální binární matici a zárove ň bere také v potaz sémantický význam prvků v těchto dvojshlucích. Ačkoliv byla práce motivována biologicky orientovanými daty, vyvinuté algoritmy jsou obecně aplikovatelné v jakémkoli jiném výzkumném oboru. Je nutné pouze dodržet požadavek na formát vstupních dat. Disertační práce představuje dva originální a v tomto ohledu i základní přístupy pro hledání sémantických dvojshluků, jako je Bicluster enrichment analysis a Rule a tree learning. Jelikož tyto metody nevyužívají vlastní hierarchické uspořádání termů v daných ontologiích, obecně je běh těchto algoritmů dlouhý čin může docházet k indukci hypotéz s redundantními termy. Z toho důvodu byl vytvořen nový operátor zjemnění. Tento operátor byl včleněn do dobře známého algoritmu CN2, kde zavádí dvě redukční procedury: Redundant Generalization a Redundant Non-potential. Obě procedury pomáhají dramaticky prořezat prohledávaný prostor pravidel a tím umožňují urychlit proces indukce pravidel v porovnání s tradičním operátorem zjemnění tak, jak je původně prezentován v CN2. Celý algoritmus spolu s redukčními metodami je publikován ve formě R balííčku, který jsme nazvali sem1R. Abychom ukázali i možnost praktického užití metody sémantického dvojshlukování na reálných biologických problémech, v disertační práci dále popisujeme a specificky upravujeme algoritmus sem1R pro dv+ úlohy. Zaprvé, studujeme praktickou aplikaci algoritmu sem1R v analýze E-3 ubikvitin ligázy v trávicí soustavě s ohledem na potenciál regenerace tkáně. Zadruhé, kromě objevování dvojshluků v dat ech genové exprese, adaptujeme algoritmus sem1R pro hledání potenciálne patogenních genetických variant v kohortě pacientů.This thesis focuses on the problem of finding interpretable and predic tive patterns, which are expressed in the form of biclusters, with an orientation to biological data. The presented methods are collectively called semantic biclustering, as a subfield of data mining. The term semantic biclustering is used here because it reflects both a process of finding coherent subsets of rows and columns in a 2-dimensional binary matrix and simultaneously takes into account a mutual semantic meaning of elements in such biclusters. In spite of focusing on applications of algorithms in biological data, the developed algorithms are generally applicable to any other research field, there are only limitations on the format of the input data. The thesis introduces two novel, and in that context basic, approaches for finding semantic biclusters, as Bicluster enrichment analysis and Rule and tree learning. Since these methods do not exploit the native hierarchical order of terms of input ontologies, the run-time of algorithms is relatively long in general or an induced hypothesis might have terms that are redundant. For this reason, a new refinement operator has been invented. The refinement operator was incorporated into the well-known CN2 algorithm and uses two reduction procedures: Redundant Generalization and Redundant Non-potential, both of which help to dramatically prune the rule space and consequently, speed-up the entire process of rule induction in comparison with the traditional refinement operator as is presented in CN2. The reduction procedures were published as an R package that we called sem1R. To show a possible practical usage of semantic biclustering in real biological problems, the thesis also describes and specifically adapts the algorithm for two real biological problems. Firstly, we studied a practical application of sem1R algorithm in an analysis of E-3 ubiquitin ligase in the gastrointestinal tract with respect to tissue regeneration potential. Secondly, besides discovering biclusters in gene expression data, we adapted the sem1R algorithm for a different task, concretely for finding potentially pathogenic genetic variants in a cohort of patients

Computationally Comparing Biological Networks and Reconstructing Their Evolution

Biological networks, such as protein-protein interaction, regulatory, or metabolic networks, provide information about biological function, beyond what can be gleaned from sequence alone. Unfortunately, most computational problems associated with these networks are NP-hard. In this dissertation, we develop algorithms to tackle numerous fundamental problems in the study of biological networks. First, we present a system for classifying the binding affinity of peptides to a diverse array of immunoglobulin antibodies. Computational approaches to this problem are integral to virtual screening and modern drug discovery. Our system is based on an ensemble of support vector machines and exhibits state-of-the-art performance. It placed 1st in the 2010 DREAM5 competition. Second, we investigate the problem of biological network alignment. Aligning the biological networks of different species allows for the discovery of shared structures and conserved pathways. We introduce an original procedure for network alignment based on a novel topological node signature. The pairwise global alignments of biological networks produced by our procedure, when evaluated under multiple metrics, are both more accurate and more robust to noise than those of previous work. Next, we explore the problem of ancestral network reconstruction. Knowing the state of ancestral networks allows us to examine how biological pathways have evolved, and how pathways in extant species have diverged from that of their common ancestor. We describe a novel framework for representing the evolutionary histories of biological networks and present efficient algorithms for reconstructing either a single parsimonious evolutionary history, or an ensemble of near-optimal histories. Under multiple models of network evolution, our approaches are effective at inferring the ancestral network interactions. Additionally, the ensemble approach is robust to noisy input, and can be used to impute missing interactions in experimental data. Finally, we introduce a framework, GrowCode, for learning network growth models. While previous work focuses on developing growth models manually, or on procedures for learning parameters for existing models, GrowCode learns fundamentally new growth models that match target networks in a flexible and user-defined way. We show that models learned by GrowCode produce networks whose target properties match those of real-world networks more closely than existing models

Preselection of Electronic Services by Given Business Service Based on Measuring Semantic Heterogeneity within the Application Area of Logistics

According to the service orientation design paradigm there are business (BS) and electronic services (ES). BS encapsulate business concerns. ES encapsulate computing systems, information systems and software applications. In environments with a high number of BS and ES the decision on which ES provides the most suitable support for a certain BS is not a trivial task. The objective of the thesis is to provide models, methods, and techniques for preselection of ES for a given BS. Preselection is about reducing the large amount of ES to a significant smaller amount under the consideration of a particular BS

Fine-Grained Provenance And Applications To Data Analytics Computation

Data provenance tools seek to facilitate reproducible data science and auditable data analyses by capturing the analytics steps used in generating data analysis results. However, analysts must choose among workflow provenance systems, which allow arbitrary code but only track provenance at the granularity of files; prove-nance APIs, which provide tuple-level provenance, but incur overhead in all computations; and database provenance tools, which track tuple-level provenance through relational operators and support optimization, but support a limited subset of data science tasks. None of these solutions are well suited for tracing errors introduced during common ETL, record alignment, and matching tasks – for data types such as strings, images, etc.Additionally, we need a provenance archival layer to store and manage the tracked fine-grained prove-nance that enables future sophisticated reasoning about why individual output results appear or fail to appear. For reproducibility and auditing, the provenance archival system should be tamper-resistant. On the other hand, the provenance collecting over time or within the same query computation tends to be repeated partially (i.e., the same operation with the same input records in the middle computation step). Hence, we desire efficient provenance storage (i.e., it compresses repeated results). We address these challenges with novel formalisms and algorithms, implemented in the PROVision system, for reconstructing fine-grained provenance for a broad class of ETL-style workflows. We extend database-style provenance techniques to capture equivalences, support optimizations, and enable lazy evaluations. We develop solutions for storing fine-grained provenance in relational storage systems while both compressing and protecting it via cryptographic hashes. We experimentally validate our proposed solutions using both scientific and OLAP workloads

Semantically defined Analytics for Industrial Equipment Diagnostics

In this age of digitalization, industries everywhere accumulate massive amount of data such that it has become the lifeblood of the global economy. This data may come from various heterogeneous systems, equipment, components, sensors, systems and applications in many varieties (diversity of sources), velocities (high rate of changes) and volumes (sheer data size). Despite significant advances in the ability to collect, store, manage and filter data, the real value lies in the analytics. Raw data is meaningless, unless it is properly processed to actionable (business) insights. Those that know how to harness data effectively, have a decisive competitive advantage, through raising performance by making faster and smart decisions, improving short and long-term strategic planning, offering more user-centric products and services and fostering innovation. Two distinct paradigms in practice can be discerned within the field of analytics: semantic-driven (deductive) and data-driven (inductive). The first emphasizes logic as a way of representing the domain knowledge encoded in rules or ontologies and are often carefully curated and maintained. However, these models are often highly complex, and require intensive knowledge processing capabilities. Data-driven analytics employ machine learning (ML) to directly learn a model from the data with minimal human intervention. However, these models are tuned to trained data and context, making it difficult to adapt. Industries today that want to create value from data must master these paradigms in combination. However, there is great need in data analytics to seamlessly combine semantic-driven and data-driven processing techniques in an efficient and scalable architecture that allows extracting actionable insights from an extreme variety of data. In this thesis, we address these needs by providing: • A unified representation of domain-specific and analytical semantics, in form of ontology models called TechOnto Ontology Stack. It is highly expressive, platform-independent formalism to capture conceptual semantics of industrial systems such as technical system hierarchies, component partonomies etc and its analytical functional semantics. • A new ontology language Semantically defined Analytical Language (SAL) on top of the ontology model that extends existing DatalogMTL (a Horn fragment of Metric Temporal Logic) with analytical functions as first class citizens. • A method to generate semantic workflows using our SAL language. It helps in authoring, reusing and maintaining complex analytical tasks and workflows in an abstract fashion. • A multi-layer architecture that fuses knowledge- and data-driven analytics into a federated and distributed solution. To our knowledge, the work in this thesis is one of the first works to introduce and investigate the use of the semantically defined analytics in an ontology-based data access setting for industrial analytical applications. The reason behind focusing our work and evaluation on industrial data is due to (i) the adoption of semantic technology by the industries in general, and (ii) the common need in literature and in practice to allow domain expertise to drive the data analytics on semantically interoperable sources, while still harnessing the power of analytics to enable real-time data insights. Given the evaluation results of three use-case studies, our approach surpass state-of-the-art approaches for most application scenarios.Im Zeitalter der Digitalisierung sammeln die Industrien überall massive Daten-mengen, die zum Lebenselixier der Weltwirtschaft geworden sind. Diese Daten können aus verschiedenen heterogenen Systemen, Geräten, Komponenten, Sensoren, Systemen und Anwendungen in vielen Varianten (Vielfalt der Quellen), Geschwindigkeiten (hohe Änderungsrate) und Volumina (reine Datengröße) stammen. Trotz erheblicher Fortschritte in der Fähigkeit, Daten zu sammeln, zu speichern, zu verwalten und zu filtern, liegt der eigentliche Wert in der Analytik. Rohdaten sind bedeutungslos, es sei denn, sie werden ordnungsgemäß zu verwertbaren (Geschäfts-)Erkenntnissen verarbeitet. Wer weiß, wie man Daten effektiv nutzt, hat einen entscheidenden Wettbewerbsvorteil, indem er die Leistung steigert, indem er schnellere und intelligentere Entscheidungen trifft, die kurz- und langfristige strategische Planung verbessert, mehr benutzerorientierte Produkte und Dienstleistungen anbietet und Innovationen fördert. In der Praxis lassen sich im Bereich der Analytik zwei unterschiedliche Paradigmen unterscheiden: semantisch (deduktiv) und Daten getrieben (induktiv). Die erste betont die Logik als eine Möglichkeit, das in Regeln oder Ontologien kodierte Domänen-wissen darzustellen, und wird oft sorgfältig kuratiert und gepflegt. Diese Modelle sind jedoch oft sehr komplex und erfordern eine intensive Wissensverarbeitung. Datengesteuerte Analysen verwenden maschinelles Lernen (ML), um mit minimalem menschlichen Eingriff direkt ein Modell aus den Daten zu lernen. Diese Modelle sind jedoch auf trainierte Daten und Kontext abgestimmt, was die Anpassung erschwert. Branchen, die heute Wert aus Daten schaffen wollen, müssen diese Paradigmen in Kombination meistern. Es besteht jedoch ein großer Bedarf in der Daten-analytik, semantisch und datengesteuerte Verarbeitungstechniken nahtlos in einer effizienten und skalierbaren Architektur zu kombinieren, die es ermöglicht, aus einer extremen Datenvielfalt verwertbare Erkenntnisse zu gewinnen. In dieser Arbeit, die wir auf diese Bedürfnisse durch die Bereitstellung: • Eine einheitliche Darstellung der Domänen-spezifischen und analytischen Semantik in Form von Ontologie Modellen, genannt TechOnto Ontology Stack. Es ist ein hoch-expressiver, plattformunabhängiger Formalismus, die konzeptionelle Semantik industrieller Systeme wie technischer Systemhierarchien, Komponenten-partonomien usw. und deren analytische funktionale Semantik zu erfassen. • Eine neue Ontologie-Sprache Semantically defined Analytical Language (SAL) auf Basis des Ontologie-Modells das bestehende DatalogMTL (ein Horn fragment der metrischen temporären Logik) um analytische Funktionen als erstklassige Bürger erweitert. • Eine Methode zur Erzeugung semantischer workflows mit unserer SAL-Sprache. Es hilft bei der Erstellung, Wiederverwendung und Wartung komplexer analytischer Aufgaben und workflows auf abstrakte Weise. • Eine mehrschichtige Architektur, die Wissens- und datengesteuerte Analysen zu einer föderierten und verteilten Lösung verschmilzt. Nach unserem Wissen, die Arbeit in dieser Arbeit ist eines der ersten Werke zur Einführung und Untersuchung der Verwendung der semantisch definierten Analytik in einer Ontologie-basierten Datenzugriff Einstellung für industrielle analytische Anwendungen. Der Grund für die Fokussierung unserer Arbeit und Evaluierung auf industrielle Daten ist auf (i) die Übernahme semantischer Technologien durch die Industrie im Allgemeinen und (ii) den gemeinsamen Bedarf in der Literatur und in der Praxis zurückzuführen, der es der Fachkompetenz ermöglicht, die Datenanalyse auf semantisch inter-operablen Quellen voranzutreiben, und nutzen gleichzeitig die Leistungsfähigkeit der Analytik, um Echtzeit-Daten-einblicke zu ermöglichen. Aufgrund der Evaluierungsergebnisse von drei Anwendungsfällen Übertritt unser Ansatz für die meisten Anwendungsszenarien Modernste Ansätze