Materialized views and indexes are physical structures for accelerating data access that are casually used in data warehouses. However, these data structures generate some maintenance overhead. They also share the same storage space. Most existing studies about materialized view and index selection consider these structures separately. In this paper, we adopt the opposite stance and couple materialized view and index selection to take view-index interactions into account and achieve efficient storage space sharing. Candidate materialized views and indexes are selected through a data mining process. We also exploit cost models that evaluate the respective benefit of indexing and view materialization, and help select a relevant configuration of indexes and materialized views among the candidates. Experimental results show that our strategy performs better than an independent selection of materialized views and indexes.
Other approaches detect common sub-expressions within workload queries in the relational context @cite_22 @cite_11 @cite_48 . The view selection problem then consists in finding common subexpressions corresponding to intermediary results that are suitable to materialize. However, browsing is very costly and these methods are not truly scalable with respect to the number of queries.
@cite_48 @cite_22 @cite_11
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Recently, multi-query optimization techniques have been considered as beneficial in view selection setting. The main interest of such techniques relies in detecting common sub expressions between the different queries of workload. This feature can be exploited for sharing updates and space storage. However, due to the reuse a query change may entail an important reorganization of the multi query graph. In this paper, we present an approach that is based on multi-query optimization for view selection and that attempts to reduce the drawbacks resulting from these techniques. Finally, we present a performance study using workloads consisting of queries over the schema of the TPC-H benchmark. This study shows that our view selection provides significant benefits over the other approaches. This paper presents an innovative approach for the publication and discovery of Web services. The proposal is based on two previous works: DIRE (DIstributed REgistry), for the user-centered distributed replication of service-related information, and URBE (UDDI Registry By Example), for the semantic-aware match making between requests and available services. The integrated view also exploits USQL (Unified Service Query Language) to provide users with a higher level and homogeneous means to interact with the different registries. The proposal improves background technology in different ways: we integrate USQL as high-level language to state service requests, widen user notifications based on URBE semantic matching, and apply URBE match making to all the facets with which services can be described in DIRE. All these new concepts are demonstrated on a simple scenario. DISCOVER operates on relational databases and facilitates information discovery on them by allowing its user to issue keyword queries without any knowledge of the database schema or of SQL. DISCOVER returns qualified joining networks of tuples, that is, sets of tuples that are associated because they join on their primary and foreign keys and collectively contain all the keywords of the query. DISCOVER proceeds in two steps. First the Candidate Network Generator generates all candidate networks of relations, that is, join expressions that generate the joining networks of tuples. Then the Plan Generator builds plans for the efficient evaluation of the set of candidate networks, exploiting the opportunities to reuse common subexpressions of the candidate networks. We prove that DISCOVER finds without redundancy all relevant candidate networks, whose size can be data bound, by exploiting the structure of the schema. We prove that the selection of the optimal execution plan (way to reuse common subexpressions) is NP-complete. We provide a greedy algorithm and we show that it provides near-optimal plan execution time cost. Our experimentation also provides hints on tuning the greedy algorithm. Database systems frequently have to execute a set of related queries, which share several common subexpressions. Multi-query optimization exploits this, by finding evaluation plans that share common results. Current approaches to multi-query optimization assume that common subexpressions are materialized. Significant performance benefits can be had if common subexpressions are pipelined to their uses, without being materialized. However, plans with pipelining may not always be realizable with limited buffer space, as we show. We present a general model for schedules with pipelining, and present a necessary and sufficient condition for determining validity of a schedule under our model. We show that finding a valid schedule with minimum cost is NP-hard. We present a greedy heuristic for finding good schedules. Finally, we present a performance study that shows the benefit of our algorithms on batches of queries from the TPCD benchmark.