There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Data Mining - Concepts and Techniques.

Schema matching is a basic problem in many database application domains, such as data integration, E-business, data warehousing, and semantic query processing. In current implementations, schema matching is typically performed manually, which has significant limitations. On the other hand, previous research papers have proposed many techniques to achieve a partial automation of the match operation for specific application domains. We present a taxonomy that covers many of these existing approaches, and we describe the approaches in some detail. In particular, we distinguish between schema-level and instance-level, element-level and structure-level, and language-based and constraint-based matchers. Based on our classification we review some previous match implementations thereby indicating which part of the solution space they cover. We intend our taxonomy and review of past work to be useful when comparing different approaches to schema matching, when developing a new match algorithm, and when implementing a schema matching component.

/pdf/a-survey-of-approaches-to-automatic-schema-matching-4n2e5qtrpt.pdf

A survey of approaches to automatic schema matching

Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaikos book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence. The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed. With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives.

Ontology Matching

A federated database system (FDBS) is a collection of cooperating database systems that are autonomous and possibly heterogeneous. In this paper, we define a reference architecture for distributed database management systems from system and schema viewpoints and show how various FDBS architectures can be developed. We then define a methodology for developing one of the popular architectures of an FDBS. Finally, we discuss critical issues related to developing and operating an FDBS.

Federated database systems for managing distributed, heterogeneous, and autonomous databases

Reliable contextual information should be generated to provide pervasive services to the occupant in smart spaces. This is difficult for several reasons. First, the number of ways to describe an event or an object is unlimited and there is no standard regarding granularity of context information in context classification schemes. Second, the quality of a given piece of contextual information is not guaranteed by uncertainty. In this paper, we propose a pragmatic context classification and a generalized context modeling scheme based on sensor fusion techniques. To make a pragmatic context classification, we introduce two approaches, “occupant-centered pragmatic approach” and “relation-dependency” approach. To improve the quality of given contextual information by reducing uncertainty, we introduce “state-space based sensor fusion modeling” as a generalized context modeling. Finally, we show an example within the applied scenario as an evidential network.

A Classification and Modeling of the Quality of Contextual Information in Smart Spaces.

In this paper, we present causes of variation in performance for passive UHF RFID tags with empirical results in two different environments: practical conditions and an anechoic chamber. We study the critical causes of RSSI ambiguity, such as a posture of tag, and variations among uniform tags. Moreover, in passive UHF RFID systems, Tag-to-Tag interferences affect performance of passive RF tags. When two tags are located close each other, an adjacent tag influences the other tags. This causes increase or decrease of the backscattering communication budgets. According to our empirical results, compared with non-interfered backscattering signal strength in an anechoic chamber, tag-to-tag interference affects the reader received signal strength, such as 5.8dB of excess decrease and 2.5dB of increase, depending on the distance between two tags. We present a new model of backscattered signal strength for passive UHF RFID system under tag-to-tag interference. The variation of excess power volume by the interference depends on an interference coefficient. In order to analyze the impacts of tag-to-tag interference, we show empirical results in the anechoic chamber, then, we model the change of the backscattered signal strength using the second order under-damped system for different tag-to-tag distances and angles.

Investigation of impact factors for various performances of passive UHF RFID system

With the emergence of World Wide Web, business' databases are increasingly being queried directly by customers. The customers may not be aware of the underlying data and its structure, and might have never learned a query language that enables them to issue structured queries. Some of the business' employees who query the databases may also not be aware of the structure of the data, but they are likely to be aware of some labels of elements containing data. We propose in this article: (1) an XML Keyword-Based search engine for answering business' customers called BusSEngine-K, and (2) an XML loosely Structured-Based search engine for answering business' employees called BusSEngine-L. The two engines employ novel context-driven search techniques and are built on top of XQuery search engine. The two engines were evaluated experimentally and compared with three recently proposed XML search engines. The results showed marked improvement.

BusSEngine: a business search engine

Advanced technologies in location acquisition allow us to track the movement of moving objects (people, planes, vehicles, animals, ships, ...) in geographical space. These technologies generate a vast amount of trajectory data (TD). Several applications in different fields can utilize such TD, for example, traffic management control, social behavior analysis, wildlife migrations and movements, ship trajectories, shoppers behavior in a mall, facial nerve trajectory, location-based services and many others. Trajectory data can be mainly handled either with Moving Object Databases (MOD) or Trajectory Data Warehouse (TDW). In this paper, we aim to review existing studies on storing, managing, and analyzing TD using data warehouse technologies. We propose a framework that aims to provide the requirements for building the TDW. Furthermore, we discuss different applications using the TDW and how these applications utilize the TDW. We address some issues with existing TDWs and discuss future work in this field.

A Survey on Trajectory Data Warehouse

Existing learning-based methods require a large number of labeled data to produce accurate part segmentation labels. However, acquiring ground truth labels is costly, giving rise to a need for methods that either require fewer labels or can utilize other currently available labels as a form of weak supervision for training. In this paper, in order to mitigate the burden of labeled-data acquisition, we propose a data-driven method for hand part segmentation on depth maps without any need for extra effort to obtain segmentation labels. The proposed method uses the labels already provided by public datasets in terms of major 3D hand joint locations to learn to estimate the hand shape and pose given a depth map. Given the pose and shape of a hand, the corresponding 3D hand mesh is generated using a deformable hand model and then rendered to a color image using a texture based on Linear Blend Skinning (LBS) weights of the hand model. The segmentation labels are then computed from the rendered color image. Since segmentation labels are not provided with current public datasets, we manually annotate a subset of the NYU dataset to perform quantitative evaluation of our method and show that a mIoU of 42% can be achieved with a model trained without using segmentation-based labels. Both qualitative and quantitative results confirm the effectiveness of our method. The code is publicly available for research purposes at: https://git.io/JmCBS.

https://dl.acm.org/doi/pdf/10.1145/3453892.3453902

Ramez Elmasri

Papers

A Classification and Modeling of the Quality of Contextual Information in Smart Spaces.

Investigation of impact factors for various performances of passive UHF RFID system

BusSEngine: a business search engine

A Survey on Trajectory Data Warehouse

Weakly-supervised hand part segmentation from depth images