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

Schema matching is the task of finding semantic correspondences between elements of two schemas. It is needed in many database applications, such as integration of web data sources, data warehouse loading and XML message mapping. To reduce the amount of user effort as much as possible, automatic approaches combining several match techniques are required. While such match approaches have found considerable interest recently, the problem of how to best combine different match algorithms still requires further work. We have thus developed the COMA schema matching system as a platform to combine multiple matchers in a flexible way. We provide a large spectrum of individual matchers, in particular a novel approach aiming at reusing results from previous match operations, and several mechanisms to combine the results of matcher executions. We use COMA as a framework to comprehensively evaluate the effectiveness of different matchers and their combinations for real-world schemas. The results obtained so far show the superiority of combined match approaches and indicate the high value of reuse-oriented strategies.

/pdf/coma-a-system-for-flexible-combination-of-schema-matching-4rgyvphehs.pdf

COMA: a system for flexible combination of schema matching approaches

Recently, schema matching has found considerable interest in both research and practice. Determining matching components of database or XML schemas is needed in many applications, e.g. for E-business and data integration. Various schema matching systems have been developed to solve the problem semi-automatically. While there have been some evaluations, the overall effectiveness of currently available automatic schema matching systems is largely unclear. This is because the evaluations were conducted in diverse ways making it difficult to assess the effectiveness of each single system, let alone to compare their effectiveness. In this paper we survey recently published schema matching evaluations. For this purpose, we introduce the major criteria that influence the effectiveness of a schema matching approach and use these criteria to compare the various systems. Based on our observations, we discuss the requirements for future match implementations and evaluations.

/pdf/comparison-of-schema-matching-evaluations-1arue36jdz.pdf

Comparison of Schema Matching Evaluations

https://dbs.uni-leipzig.de/file/IS2007.pdf

Matching large schemas: Approaches and evaluation

Over the next decade, it is likely that science and engineering research will produce more scientific data than has been created over the whole of human history. The successful use of these data to achieve new scientific breakthroughs will depend on the ability to access, integrate, and analyze these large datasets. Robust data organization and publication methods are needed within the research community to enable data discovery and scientific analysis by researchers other than those that collected the data. We present a new method for publishing research datasets consisting of point observations that employs a standard observations data model populated using controlled vocabularies for environmental and water resources data along with web services for transmitting data to consumers. We describe how these components have reduced the syntactic and semantic heterogeneity in the data assembled within a national network of environmental observatory test beds and how this data publication system has been used to create a federated network of consistent research data out of a set of geographically decentralized and autonomous test bed databases.

/pdf/an-integrated-system-for-publishing-environmental-24im9qkcwh.pdf

An integrated system for publishing environmental observations data

Semantic Integration of Heterogeneous Information Sources.

Towards Design Principles for a Real-Time Anomaly Detection Algorithm Benchmark Suited to Industrie 4.0 Streaming Data

Improving Machine Self-Diagnosis with an Instance-Based Selector for Real-Time Anomaly Detection Algorithms

The digitalization entails a significant increase of information that can be used for decision-making in many business areas. In production, the proliferation of smart sensor technology leads to the real-time availability of manifold information from entire production environments. Due to the digitalization, many decision processes are automated. However, humans are supposed to remain at the center of decision-making to steer production. One central area of decision-making in digitalized production is real-time anomaly detection. Current implementations mainly focus on finding anomalies in sensor data streams. This research goes a step further by presenting design, prototypical implementation and evaluation of a real-time semantic anomaly labeler. The core functionality is to provide semantic annotations for anomalies to enable humans to make more informed decisions in real-time. The resulting implementation is flexibly applicable as it uses local data features to distinguish kinds of anomalies that receive different labels. Demonstration and evaluation show that the resulting implementation is capable of reliably labelling anomalies of different kinds from production processes in real-time with high precision. 

A Real-Time Semantic Anomaly Labeler Capturing Local Data Stream Features to Distinguish Anomaly Types in Production

Industry 4.0 describes flexibly combinable production machines enabling efficient fulfillment of individual requirements. Timely and automated anomaly recognition by means of machine self-diagnosis might support efficiency. Various algorithms have been developed in recent years to detect anomalies in data streams. Due to their diverse functionality, the application of different real-time anomaly detection algorithms to the same data stream may lead to different results. Existing algorithms as well as mechanisms for their evaluation and selection are context-independent and not suited to industry 4.0 settings. In this research paper, an industry 4.0 specific benchmark for real-time anomaly detection algorithms is developed on the basis of six design principles in the categories timeliness, threshold setting and qualitative classification. Given context-specific input parameters, the benchmark ranks algorithms according to their suitability for real-time anomaly detection in production datasets. The application of the benchmark is demonstrated and evaluated on the basis of two case studies. 

Bodo Rieger

Papers

Semantic Integration of Heterogeneous Information Sources.

Towards Design Principles for a Real-Time Anomaly Detection Algorithm Benchmark Suited to Industrie 4.0 Streaming Data

Improving Machine Self-Diagnosis with an Instance-Based Selector for Real-Time Anomaly Detection Algorithms

A Real-Time Semantic Anomaly Labeler Capturing Local Data Stream Features to Distinguish Anomaly Types in Production

A Benchmark for Real-Time Anomaly Detection Algorithms Applied in Industry 4.0