Query Rewriting with Thesaurus-Based for Handling Semantic Heterogeneity in Database Integration

Due to the rapid scientific development, ontologies and schemata need to change. When ontologies evolve, the changes should somehow be rendered and used by the pre-existing data integration systems. In most of these systems, when ontologies change their relations with the data sources i.e. the mappings, are recreated manually, a process which is known to be error-prone and time-consuming.In this dissertation, we provide a solution that allows query answering under evolving ontologies without mapping redefinition. This is achieved by exploiting query rewriting. We elegantly separate the semantics of query rewriting for different ontology versions and for the sources and we present a module that enables ontology evolution over traditional ontology-based data integration systems. That module gets as input the different ontology versions and the user query, and rewrites the query over data integration systems that use different ontology versions. This is performed by the automatic identification of changes among the ontology versions using a high-level language of changes, which are then interpreted as GAV mappings to enable query rewriting among ontology versions.Although query rewriting always succeeds, several problems may occur due to non information preserving changes among the ontology versions. We identify the problems in such a setting and we provide efficient, intuitive solutions, either by explaining the reasons for the failure or by producing the best “over-approximations”.We prove that our approach imposes only a small overhead over traditional query rewriting algorithms and it is modular and scalable. Finally, we show that it can greatly reduce human effort spent since continuous mapping redefinition on evolving ontologies is no longer necessary.

Query rewriting method is proposed for the heterogeneous information integration infrastructure formed by the subject mediator environment. Local as View (LAV) approach treating schemas exported by sources as materialized views over virtual classes of the mediator is considered as the basis for the subject mediation infrastructure. In spite of significant progress of query rewriting with views, it remains unclear how to rewrite queries in the typed, object-oriented mediator environment. This paper embeds conjunctive views and queries into an advanced canonical object model of the mediator. The “selection-projection-join” (SPJ) conjunctive query semantics based on type specification calculus is introduced. The paper demonstrates how the existing query rewriting approaches can be extended to be applicable in such typed environment. The paper shows that refinement of the mediator class instance types by the source class instance types is the basic relationship required for establishing query containment in the object environment.KeywordsCanonical ModelConjunctive QueryAbstract Data TypeVery Large Data BaseSkolem FunctionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

To integrate relational data semantically, schema description and query accessing issues are discussed. Ontology is chosen as the global schema to describing semantics. To building domain ontology rapidly, an approach based on subgraph extraction is presented. Then the semantics of SAPRQL queries is defined and algorithm for query rewriting is put forward. Minimal connectable unit is defined to represent a fragment of semantic mapping from the query to the rewriting. Finding and joining minimal connectable units produce semantically correct query plans. Soundness and completeness of query rewriting algorithm is proved. Computational complexity of the algorithm is discussed. Under the worst case, the query rewriting algorithm requires O(n m ) time. Experiment results show that the query processing algorithm can provide nice performance.

Machine learning (ML) training and scoring fundamentally relies on linear algebra programs and more general tensor computations. Most ML systems utilize distributed parameter servers and similar distribution strategies for mini-batch stochastic gradient descent training. However, many more tasks in the data science and engineering lifecycle can benefit from efficient tensor computations. Examples include primitives for data cleaning, data and model debugging, data augmentation, query processing, numerical simulations, as well as a wide variety of training and scoring algorithms. In this survey tutorial, we first make a case for the importance of optimizing more general tensor computations, and then provide an in-depth survey of existing applications, optimizing compilation techniques, and underlying runtime strategies. Interestingly, there are close connections to data-intensive applications, query rewriting and optimization, as well as query processing and physical design. Our goal for the tutorial is to structure existing work, create common terminology, and identify open research challenges.

The response phase in a disaster case is often considered to be the most critical in terms of saving lives and dealing with irreversible damage. The timely provision of geospatial information is crucial in the decision-making process. Thus, there is a need for the integration of heterogeneous spatial databases which are inherently distributed and created under different projects by various organizations. The integration of all relevant data for timely decision making is a challenging task due to syntactic, schematic and semantic heterogeneity. This paper aims to propose a framework for the integration of heterogeneous spatial databases using novel approaches, such as web services and ontologies. We focus on providing solutions for the three levels of heterogeneity, in order to be able to interrogate the content of the different databases conveniently. Based on the proposed framework, we implemented a use case using heterogeneous data belonging to La Rochelle city in France.

The emergence of the Semantic Web has given rise to researches on semantic integration of heterogeneous relational databases using ontology. Semantic-based heterogeneous relational data integration is recently attracting many diverse attentions, since most of the information is still stored in relational databases. This paper focuses on the query processing problem in semantic-based heterogeneous relational databases integration systems. The semantic of query rewriting is defined and algorithms for query decomposing, rewriting and transforming are presented. SPARQL queries over global schema are rewritten into local SQL queries that can be executed on heterogeneous relational databases. The experiment results show that the approach we proposed has nice performance and scalability.

Image analysis is an interesting research area with a large variety of challenging applications. Researchers have worked from decades on this topic, as witnessed by the scientific literature. However, document image analysis is the special case in image analysis as their spatial properties are different from natural images. Therefore, the main focus of this paper is to describe image denoising issues in general and document image issues in particular. Since the field of document processing is relatively new, it is also dynamic, so current methods have room for improvement and innovations are still being made. Several algorithms proposed in the literature are described. Critical discussions are reported about the current status of the field and open problems are highlighted. It is also demonstrated that, there are rarely definitive techniques for all cases of a certain problem. We surveyed the state of art, analyzed recent trends and tried to identify challenges for future research in this field.

Many problem settings in machine learning are concerned with the simultaneous prediction of multiple target variables of diverse type. Amongst others, such problem settings arise in multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. These subfields of machine learning are typically studied in isolation, without highlighting or exploring important relationships. In this paper, we present a unifying view on what we call multi-target prediction (MTP) problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research.

Solar photovoltaic technology is based on the direct conversion of solar radiation power into electrical power by means of solar cells. Photovoltaics can in principle play a major role in the search for alternative energy sources, even though it is still not competitive with respect to traditional sources, e.g. based on fossil fuels. One of the most important challenges of current scientific research in this field relates to the development of new solar cell materials or new cell architectures, possibly leading to an improvement of the cell power conversion efficiency or to the parallel reduction of the energy production cost; nanotechnology plays a leading role in this framework. In this paper selected examples will be presented, which show the possible contribution of nanoscience and nanotechnology to the study and optimization of nanostructured materials for better future generation solar cells. After an introductory part presenting the different types of solar cells and highlighting the role of nanotechnology research in this field, two specific cases will be presented and discussed: the study of hierarchical titanium oxide nanostructures as photoanodes for novel dye-sensitized solar cells and the development of nanostructured transparent conducting oxides for designing multifunctional electrodes for organic or hybrid photovoltaic cells.

Blockchain (BC) represents a distributed ledger technology that has been utilized for providing security and privacy in distributed networks. This makes it applicable to the distributed nature of IoT, which still suffers from privacy and security vulnerabilities. However, the core BC technology is computationally expensive and commonly involves high bandwidth overhead and delays not suitable for IoT related scenarios. In order to foster the synergy between BC and IoT, recent research advancements have specifically focused on developing novel BC approaches that are tailored to the requirements and needs of the specific IoT use cases. The paper elaborates the BC–IoT related issues and provides a comprehensive survey of the current literature and relevant initiated deployments. The paper also identifies the key research and development challenges and discusses the possible aspects for future research.

Purpose– The purpose of this exploratory paper is to examine the lack of reliability of traditional neo-classical models and to argue that it is due to the hidden complexity and non-linearity that may operate at times in residential housing markets. As a result, market efficiency may be a special case, rather than the prevailing rule. An alternative framework that incorporates the higher order concepts of complexity – based on the non-linear, emergent behavior of multiple agents – is required to model discontinuities and imbalances in the housing markets.Design/methodology/approach– The paper examines the building block concepts required to model the complexity of the housing market and analyzes their implications. These implications can be counter-intuitive and help explain the failure of policy makers to model the recent bust in global housing markets.Findings– The paper finds that policy makers need to adopt an analytical framework that incorporates non-linearity, emergence and other building blocks of complexity in order to construct representative financial models that help understand systemic imbalances that may afflict residential housing markets.Originality/value– This is the first paper to one's knowledge that argues that policy makers should adopt an alternative theoretical framework based on complexity concepts in order to create more effective financial models; such models should include indicators that provide early warning signals of potential discontinuities in housing markets.

Given the proliferation of wireless sensors and smart mobile devices, an explosive escalation of the volume of data is anticipated. However, restricted by their limited physical sizes and low manufacturing costs, these wireless devices tend to have limited computational capabilities and battery lives. To overcome this limitation, wireless devices may offload their computational tasks to the nearby computing nodes at the network edge in mobile edge computing (MEC). At the time of writing, the benefits of MEC systems have not been fully exploited, predominantly because the computation offloading link is still far from perfect. In this article, we propose to enhance MEC systems by exploiting the emerging technique of reconfigurable intelligent surfaces (RISs), which are capable of “reconfiguring” the wireless propagation environments, hence enhancing the offloading links. The benefits of RISs can be maximized by jointly optimizing both the RISs as well as the communications and computing resource allocations of MEC systems. Unfortunately, this joint optimization imposes new research challenges on the system design. Against this background, this article provides an overview of RIS-assisted MEC systems and highlights their four use cases as well as their design challenges and solutions. Finally, their performance is characterized with the aid of a specific case study, followed by a range of future research ideas.

Context Interchange Mediation for Semantic Interoperability and Dynamic Integration of Autonomous Information Sources in the Fixed Income Securities Industry

The introduction of English as a foreign language in many countries at ever younger ages poses new challenges for teachers, material designers and researchers in terms of finding out and dealing with the needs of such young children when learning a foreign language. This paper focuses on vocabulary learning at early ages, arguing for the inclusion of figurative language in young learners’ English input for two main reasons: firstly, some figurative uses are very frequent in general language and suitable for young learners’ communicative needs and, secondly, research (Piquer-Píriz, 2005) has shown that children as young as 5 years old are able to work out figurative extensions of words whose literal meanings they know, through associative (metonymic) and analogical (metaphoric) reasoning. The specific case of hand is explored, drawing on some of the findings mentioned above, to discuss the potential of cognitive linguistic-inspired approaches in the young learner classroom.

Narratives of Storytelling Across Cultures demonstrates how meaning found within interpersonal communication is not universal across all cultures. Miscommunication can occur when the foundations of cultural meaning within stories, as told socially and within media, vary among different cultures. Positioned within the communication and media field, this book connects issues of societal tension and political battles to media portrayals, social communication events, and power dynamics that result when people with different meanings systems attempt to negotiate "truth" among their competing narratives. After establishing the theoretical foundation of the book, contributors provide specific case studies that demonstrate underlying cultural components and complexities that lead to these issues. Tony R. DeMars and Gabriel Tait have assembled contributors with research, experience, and understanding of intercultural communication challenges in different social groups, allowing the book to take on a broader scope of intercultural communication. Scholars of communication, conflict resolution, political science, sociology, and media studies will find this book particularly useful.