Compensation methods to support cooperative applications: A case study in automated verification of schema requirements for an advanced transaction model

Compensation plays an important role in advanced transaction models, cooperative work and workflow systems. A schema designer is typically required to supply for each transaction another transaction to semantically undo the effects of . Little attention has been paid to the verification of the desirable properties of such operations, however. This paper demonstrates the use of a higher-order logic theorem prover for verifying that compensating transactions return a database to its original state. It is shown how an OODB schema is translated to the language of the theorem prover so that proofs can be performed on the compensating transactions

Compensation methods to support generic graph editing: A case study in automated verification of schema requirements for an advanced transaction model

Compensation plays an important role in advanced transaction models, cooperative work, and workflow systems. However, compensation operations are often simply written as a^−1 in transaction model literature. This notation ignores any operation parameters, results, and side effects. A schema designer intending to use an advanced transaction model is expected (required) to write correct method code. However, in the days of cut-and-paste, this is much easier said than done. In this paper, we demonstrate the feasibility of using an off-the-shelf theorem prover (also called a proof assistant) to perform automated verification of compensation requirements for an OODB schema. We report on the results of a case study in verification for a particular advanced transaction model that supports cooperative applications. The case study is based on an OODB schema that provides generic graph editing functionality for the creation, insertion, and manipulation of nodes and links

Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval

Where previous reviews on content-based image retrieval emphasize on what can be seen in an image to bridge the semantic gap, this survey considers what people tag about an image. A comprehensive treatise of three closely linked problems, i.e., image tag assignment, refinement, and tag-based image retrieval is presented. While existing works vary in terms of their targeted tasks and methodology, they rely on the key functionality of tag relevance, i.e. estimating the relevance of a specific tag with respect to the visual content of a given image and its social context. By analyzing what information a specific method exploits to construct its tag relevance function and how such information is exploited, this paper introduces a taxonomy to structure the growing literature, understand the ingredients of the main works, clarify their connections and difference, and recognize their merits and limitations. For a head-to-head comparison between the state-of-the-art, a new experimental protocol is presented, with training sets containing 10k, 100k and 1m images and an evaluation on three test sets, contributed by various research groups. Eleven representative works are implemented and evaluated. Putting all this together, the survey aims to provide an overview of the past and foster progress for the near future.Comment: to appear in ACM Computing Survey