Multi-criteria decision making has been made possible with the advent of skyline queries. However, processing such queries for high dimensional datasets remains a time consuming task. Real-time applications are thus infeasible, especially for non-indexed skyline techniques where the datasets arrive online. In this paper, we propose a caching mechanism that uses the semantics of previous skyline queries to improve the processing time of a new query. In addition to exact queries, utilizing such special semantics allow accelerating related queries. We achieve this by generating partial result sets guaranteed to be in the skyline sets. We also propose an index structure for efficient organization of the cached queries. Experiments on synthetic and real datasets show the effectiveness and scalability of our proposed methods.
@cite_7: Semantic caching is very attractive for use in distributed systems due to the reduced network traffic and the improved response time. It is particularly efficient for a mobile computing environment, where the bandwidth of wireless links is a major performance bottleneck. Previous work either does not provide a formal semantic caching model, or lacks efficient query processing strategies. This paper extends the existing research in three ways: formal definitions associated with semantic caching are presented, query processing strategies are investigated and, finally, the performance of the semantic cache model is examined through a detailed simulation study.
@cite_8: Given a query tuple q, the dynamic skyline query retrieves the tuples that are not dynamically dominated by any other in the data set with respect to q. A tuple dynamically dominates another, w.r.t. q, if it has closer to q's values in all attributes, and has strictly closer to q's value in at least one. The dynamic skyline query can be treated as a standard skyline query, subject to the transformation of all tuples' values. In this work, we make the observation that results to past dynamic skyline queries can help reduce the computation cost for future queries. To this end, we propose a caching mechanism for dynamic skyline queries and devise a cache-aware algorithm. Our extensive experimental evaluation demonstrates the efficiency of this mechanism compared to standard techniques without caching.
@cite_9: Independent optimization of database requests overlooks potential savings which can be achieved when they are optimized collectively. An intuitive model for queries called the query graph supports common expression detection for optimization of a stream of requests. We describe how ad hoc query processing can be improved using intermediate results and answers produced from earlier queries, without significantly impacting processing costs when no common expressions are found. We have written a Pascal program, COMMON, which implements a variation of the algorithm which we describe.
@cite_0: We propose a semantic model for client-side caching and replacement in a client-server database system and compare this approach to page caching and tuple caching strategies. Our caching model is based on, and derives its advantages from, three key ideas. First, the client maintains a semantic description of the data in its cache,which allows for a compact specification, as a remainder query, of the tuples needed to answer a query that are not available in the cache. Second, usage information for replacement policies is maintained in an adaptive fashion for semantic regions, which are associated with collections of tuples. This avoids the high overheads of tuple caching and, unlike page caching, is insensitive to bad clustering. Third, maintaining a semantic description of cached data enables the use of sophisticated value functions that incorporate semantic notions of locality, not just LRU or MRU, for cache replacement. We validate these ideas with a detailed performance study that includes traditional workloads as well as a workload motivated by a mobile navigation application.
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@cite_15: The emergence of query-based online data services and e-commerce applications has prompted much recent research on data caching. This article studies semantic caching, a caching architecture for such applications, that caches the results of selection queries. The primary contribution of this article is to revisit the performance and overhead of semantic caching using a modern database server and modern hardware. Initially, the performance study focuses on simple workloads and demonstrates several benefits of semantic caching, including low overhead, insensitivity to the physical layout of the database, reduced network traffic, and the ability to answer some queries without contacting the server. With moderately complex workloads, careful coding of remainder queries is required to maintain efficient query processing at the server. Using very complex workloads, we demonstrate that semantic caching works well in a range of applications, especially in network-constrained environments.
The idea of caching query results to optimize subsequent query processing was first studied in @cite_9 @cite_2 . Several algorithms have been proposed in @cite_0 @cite_7 @cite_0 @cite_15 that uses semantic caching efficiently and effectively for general applications. Also dynamic caching policies have been studied @cite_8 .