The dynamics of belief and knowledge is one of the major components of any autonomous system that should be able to incorporate new pieces of information. We show that knowledge base dynamics has interesting connection with kernel change via hitting set and abduction. The approach extends and integrates standard techniques for efficient query answering and integrity checking. The generation of hitting set is carried out through a hyper tableaux calculus and magic set that is focussed on the goal of minimality. Many different view update algorithms have been proposed in the literature to address this problem. The present paper provides a comparative study of view update algorithms in rational approach.
Abstract We present an approach to updating deductive databases in which every insertion or deletion of a fact (atomic formula without variables) can be performed in a deterministic way. The main features of our approach are the following: (i) the inserted and deleted facts may concern any predicate of the underlying alphabet—not just extensional predicates, and (ii) deleted facts are explicitly stored in the database. We show that logic programs in our approach can be associated with well-founded semantics. Moreover, as the explicit storage of deleted facts introduces a significant overhead, we also study the problem of storage optimization. A data warehouse stores information that is collected from multiple, heterogeneous information sources for the purpose of complex querying and analysis. Information in the warehouse is typically stored in the form of materialized views, which represent pre-computed portions of frequently asked queries. One of the most important tasks when designing a warehouse is the selection of materialized views to be maintained in the warehouse. The goal is to select a set of views in such a way as to minimize the total query response time over all queries, given a limited amount of time for maintaining the views (maintenance-cost view selection problem). In this paper, we propose an efficient solution to the maintenance-cost view selection problem using a genetic algorithm for computing a near-optimal set of views. Specifically, we explore the maintenance-cost view selection problem in the context of OR view graphs. We show that our approach represents a dramatic improvement in time complexity over existing search-ba... Data Warehouse applications use a large number of materialized views to assist a Data Warehouse to perform well. But how to select views to be materialized is challenging. Several heuristic algorithms have been proposed in the past to tackle with this problem. In this paper, we propose a completely different approach, Genetic Algorithm, to choose materialized views and demonstrate that it is practical and effective compared with heuristic approaches. In this paper, a new syntax-based approach to belief revision is presented. It is developed within a nonmonotonic framework that allows a two-steps handling of inconsistency to be adopted. First, a disciplined use of non-monotonic ingredients is made available to the knowledge engineer to prevent many inconsistencies that would occur if a standard logical interpretation and representation of beliefs were conducted. Remaining inconsistencies are considered unexpected and revised by weakening the formulas occurring in any minimally inconsistent subbase, as if they were representing exceptional cases that do not actually occur. While the computation of revised knowledge bases remains intractable in the worst case, our approach benefits from an efficient local search-based heuristic technique that empirically proves often viable, even in the context of very large prepositional applications.   The problem of answering queries using views is to find efficient methods of answering a query using a set of previously defined materialized views over the database, rather than accessing the database relations. The problem has recently received significant attention because of its relevance to a wide variety of data management problems. In query optimization, finding a rewriting of a query using a set of materialized views can yield a more efficient query execution plan. To support the separation of the logical and physical views of data, a storage schema can be described using views over the logical schema. As a result, finding a query execution plan that accesses the storage amounts to solving the problem of answering queries using views. Finally, the problem arises in data integration systems, where data sources can be described as precomputed views over a mediated schema. This article surveys the state of the art on the problem of answering queries using views, and synthesizes the disparate works into a coherent framework. We describe the different applications of the problem, the algorithms proposed to solve it and the relevant theoretical results. During the process of updating a database, two interrelated problems could arise. On one hand, when an update is applied to the database, integrity constraints could become violated, thus falsifying database consistency. In this case, the integrity constraint maintenance approach tries to obtain additional updates to be applied to re-establish database consistency. On the other hand, when an update request consist on updating some derived predicate, a view updating mechanism must be applied to translate the update request into correct updates on the underlying base facts. In this paper, we propose a general framework to compare and classify current methods in the field of view updating and integrity constraint maintenance. In this sense, we classify them considering how they tackle with both problems and, we also state the main drawbacks these methods have.  Materialized view selection is a critical problem in many applications such as query processing, data warehousing, distributed and semantic web databases, etc. We refer to the problem of selecting an appropriate set of materialized views as the view selection problem. Many different view selection methods have been proposed in the literature to address this issue. The present paper provides a survey of view selection methods. It defines a framework for highlighting the view selection problem by identifying the main dimensions that are the basis in the classification of view selection methods. Based on this classification, this study reviews most of the view selection methods by identifying respective potentials and limits. XML has become the de facto standard for representing and interchanging data in web-based applications. And XML view, a virtual window for specified users, has been widely applied. In practical system, users encounter the so-called view update problem when they need update source data through the view. For a long time, the view update problem is an open question in database community. With the development of various data models, the corresponding view update problem has been widely researched. In this paper, we introduce the conception of view update problem. We survey and compare previous methods for resolving it. Especially, we emphasize the role of semantics. Focusing on the problem in XML context, we analyze it and propose a framework, which collects the semantic information at view definition time.