View materialization is an important technique for high performance query processing, data integration and replication. Solutions to the problem of incrementally maintaining materialized views are very relevant. So far, most work on this problem has been confined to relational settings and solutions have not been comprehensively evaluated. This paper describes MOVIE, a complete, implemented and evaluated solution to the problem of incrementally maintaining materialized OQL views in ODMG-compliant object databases. The evaluation throws light into how the e#ectiveness of incremental maintenance is a#ected by issues such as database size, and the complexity and selectivity of views.

In this paper, we address an optimization problem that arises in context of cache placement in sensor networks. In particular, we consider the cache placement problem where the goal is to determine a set of nodes in the network to cache/store the given data item, such that the overall communication cost incurred in accessing the item is minimized, under the constraint that the total communication cost in updating the selected caches is less than a given constant. In our network model, there is a single server (containing the original copy of the data item) and multiple client nodes (that wish to access the data item). For various settings of the problem, we design optimal, near-optimal, heuristic-based, and distributed algorithms, and evaluate their performance through simulations on randomly generated sensor networks. 1

Introduction Information is one of the most valuable assets of an organization and when used properly can assist intelligent decision making that can significantly improve the functioning of an organization. Data warehousing is a recent technology that allows information to be easily and efficiently accessed for decision making activities. On-Line Analytical Processing (OLAP) tools are wellstudied for complex data analysis. A data warehouse is a set of subject-oriented, integrated, time varying, and non-volatile databases used to support the decision-making activities [42]. The conceptual architecture of a data warehousing system is shown in Figure 1. The data Information Sources - Relational -Legacy Warehouse Meta Data - Select -Transform -Clean -Integrate -Refresh -Others -Network Data OLAP Server MOLAP ROLAP/ Clients Front-end Tools -Analysis -Data Mining -Report generator - Others and Management Component Warehouse Creation

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. Keywords: Data mining, Cost models.

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