Schema matching identifies elements of two given schemas that correspond to each other. Although there are many algorithms for schema matching, little has been written about building a system that can be used in practice. We describe our initial experience building such a system, a customizable schema matcher called Protoplasm. Schema MatchingMost systems-integration work requires creating mappings between models, such as database schemas, message formats, interface definitions, and user-interface forms. As a design activity, schema mapping is similar to database design in that it requires digging deeply into the semantics of schemas. This is usually quite time consuming, not only in level-of-effort but also in elapsedtime, because the process of teasing out semantics is slow. It can benefit from the development of improved tools, but it is unlikely such tools will provide a silver bullet that automates all of the work. For example, it is hard to imagine how the best tool could eliminate the need for a designer to read documentation, ask developers and endusers how they use the data, or review test runs to check for unexpected results. In a sense, the problem is AI-complete, that is, as hard as reproducing human intelligence.The best commercially-available model mapping tools we know of are basically graphical programming tools. That is, they allow one to specify a schema mapping as a directed graph where nodes are simple data transformations and edges are data flows. Such tools help specify: a mapping between two messages, a data warehouse loading script, or a database query. While such graphical programming is an improvement over typing code, no database design intelligence is being offered. Despite the limited expectations expressed in the previous paragraph, we should certainly be able to offer some intelligence automated help, in addition to attractive graphics.There are two steps to automating the creation of mappings between schemas: schema matching and query discovery. Schema matching identifies elements that correspond to each other, but does not explain how they correspond. For example, it might say that FirstName and LastName in one schema are related to Name in the other, but not say that concatenating the former yields the latter. Query discovery picks up where schema matching leaves off. Given the correspondences, it obtains queries to translate instances of the source schema into instances of the target, e.g., using query analysis and data mining [5].This paper focuses on schema matching. There are many algorithms to solve it [8]. They exploit name similarity, thesauri, schema structure, instances, value distribution of instances, past mappings, constraints, cluster analysis of a schema corpus, and similarity to standard schemas. All of these algorithms have merit. So what we need is a toolset that incorporates them in an integrated package. This is the subject of this paper.The published work on schema matching is mostly about algorithms, not systems. This algorithm work is helpful, off...
Abstract. The insider threat against database management systems is a dangerous security problem. Authorized users may abuse legitimate privileges to masquerade as another user or to maliciously harvest data. We propose a new direction to address the problem. We model users' access patterns by profiling the data points that users access, in contrast to analyzing the query expressions in prior approaches. Our data-centric approach is based on the key observation that query syntax alone is a poor discriminator of user intent, which is much better rendered by what is accessed. We present a feature-extraction method to model users' access patterns. Statistical learning algorithms are trained and tested using data from a real Graduate Admission database. Experimental results indicate that the technique is very effective, accurate, and is promising in complementing existing database security solutions. Practical performance issues are also addressed.
Data services key insightsData services differ from traditional Web services in that they serve as "fronts" for data and are based on a richer model of that data.The growing importance of data services in the movement toward a hosted-services world is evidenced by the number of contexts within which they have been utilized in recent years: data publishing, data exchange and integration, service-oriented architectures (soA), data as a service (Daas), and most recently, cloud computing.While data services were initially conceived to solve problems in the enterprise world, the cloud is now making data services accessible to a much broader range of consumers.
The CLIDE System assists the owners of sources that participate in Web service-based data publishing systems to publish a restricted set of parameterized queries over the schema of their sources and package them as WSDL services. The sources may be relational databases, which naturally have a schema, or ad hoc information/application systems whereas the owner publishes a virtual schema. CLIDE allows information clients to pose queries over the published schema and utilizes prior work on answering queries using views to answer queries that can be processed by combining and processing the results of one or more Web service calls. These queries are called feasible. Contrary to prior work, where infeasible queries are rejected without an explanatory feedback, leading the user into a frustrating trial-and-error cycle, CLIDE features a query formulation interface, which extends the QBE-like query builder of Microsoft's SQL Server with a color scheme that guides the user toward formulating feasible queries. CLIDE guarantees that the suggested query edit actions are complete (i.e., each feasible query can be built by following only suggestions), rapidly convergent (the suggestions are tuned to lead to the closest feasible completions of the query), and suitably summarized (at each interaction step, only a minimal number of actions needed to preserve completeness are suggested). We present the algorithms, implementation, and performance evaluation showing that CLIDE is a viable on-line tool. ACM Reference Format:Petropoulos, M., Deutsch, A., Papakonstantinou, Y., and Katsis, Y. 2007. Exporting and interactively querying web service-accessed sources: The CLIDE system.
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