This study is focused on improving the reactivity of a CaO sorbent for its use in a reactionbased process for the separation of carbon dioxide (CO 2 ) from flue gas. The separation process consists of cyclical carbonation (of a metal oxide) and calcination (of the metal carbonate formed) reactions to yield concentrated CO 2 from flue gas. CaO sorbents synthesized from naturally occurring limestone and dolomite were microporous in nature. Pore filling and pore pluggage of these micropores limited the conversion of CaO in the carbonation reaction to about 45-50% of the stoichiometric limit. A wet precipitation process was tailored to synthesize high-surfacearea precipitated calcium carbonate (PCC). The pores of PCC predominantly lie in the mesoporous range (5-20 nm). The CaO sorbent obtained from PCC (PCC-CaO) was less susceptible to pore pluggage and attained over 90% conversion. PCC-CaO was also capable of maintaining its high reactivity (>90%) over two carbonation-calcination cycles.

Spatial query execution is an essential functionality of a sensor network, where a query gathers sensor data within a specific geographic region. Redundancy within a sensor network can be exploited to reduce the communication cost incurred in execution of such queries. Any reduction in communication cost would result in an efficient use of the battery energy, which is very limited in sensors. One approach to reduce the communication cost of a query is to self-organize the network, in response to a query, into a topology that involves only a small subset of the sensors sufficient to process the query. The query is then executed using only the sensors in the constructed topology.In this article, we design and analyze algorithms for such self-organization of a sensor network to reduce energy consumption. In particular, we develop the notion of a connected sensor cover and design a centralized approximation algorithm that constructs a topology involving a nearoptimal connected sensor cover. We prove that the size of the constructed topology is within an O(log n) factor of the optimal size, where n is the network size. We also develop a distributed self-organization version of our algorithm, and propose several optimizations to reduce the communication overhead of the algorithm. Finally, we evaluate the distributed algorithm using simulations and show that our approach results in significant communication cost reduction.

In optimization studies including multi-objective optimization, the main focus is placed on finding the global optimum or global Pareto-optimal solutions, representing the best possible objective values. However, in practice, users may not always be interested in finding the so-called global best solutions, particularly when these solutions are quite sensitive to the variable perturbations which cannot be avoided in practice. In such cases, practitioners are interested in finding the robust solutions which are less sensitive to small perturbations in variables. Although robust optimization is dealt with in detail in single-objective evolutionary optimization studies, in this paper, we present two different robust multi-objective optimization procedures, where the emphasis is to find a robust frontier, instead of the global Pareto-optimal frontier in a problem. The first procedure is a straightforward extension of a technique used for single-objective optimization and the second procedure is a more practical approach enabling a user to set the extent of robustness desired in a problem. To demonstrate the differences between global and robust multi-objective optimization principles and the differences between the two robust optimization procedures suggested here, we develop a number of constrained and unconstrained test problems having two and three objectives and show simulation results using an evolutionary multi-objective optimization (EMO) algorithm. Finally, we also apply both robust optimization methodologies to an engineering design problem.

Abstract. A data warehouse stores materialized views of data from one or more sources, with the purpose of efficiently implementing decisionsupport or OLAP queries. One of the most important decisions in designing a data warehouse is the selection of materialized views to be maintained at the warehouse. The goal is to select an appropriate set of views that minimizes total query response time and the cost of maintaining the selected views, given a limited amount of resource, e.g., materialization time, storage space etc. In this article, we develop a theoretical framework for the general problem of selection of views in a data warehouse. We present competitive polynomial-time heuristics for selection of views to optimize total query response time, for some important special cases of the general data waxehouse scenario, viz.: (i) an AND view graph, where each query/view has a unique evaluation, and (ii) an OR view graph, in which any view can be computed from any one of its related views, e.g., data cubes. We extend the algorithms to the case when there is a set of indexes associated with each view. Finally, we extend our heuristic to the most general case of AND-OR view graphs.

IntroductionOn-line analytical processing (OLAP) is a recent and important application of database systems. Typically, O L A P data is presented as a multidimensional "data cube." O L A P queries are complex and can take many hours or even days to run, if executed directly on the raw data. The most common method of reducing execution time is to precompute some of the queries into summary tables (subcubes of the data cube) and then to build indexes on these summary tables. In most coinmercaal OLAP systems today, the summary tables that are to be precomputed are picked first, followed b y the selection of the appropriate indexes on them.A trial-and-error approach is used to divide the space available between the summary tables and the indexes. This two-step process can perform very poorly. Since both summary tables and indexes consume the same resource -space -their selection should be done together for the most eficient use of space. In this payer, we give algorithms that automate the selection of sumrria'ry tables and zndexes. In particular, we present a family of algorathms of increasing time complexities, and pro~iie strong performance bounds for them. The algorithms with higher complexities have better performance borinds. However, the increase in the performance bound is diminishing, and we show that an algorithm of moderate complexity can perform fairly close to th,e optim,al.Decision-support systems are an increasingly iinportant application of databases. Corporations are beginning to use the accumulated operational data to help understand and run their business. Towards this purpose, data from the different operations of a corporation are reconciled and stored in a central database commonly called a "data warehouse." Analysts use the data warehouse to extract the business information that enables better decision making. This interactive decision-support process is called OLAP (On-line Analytical Processing) to distinguish it from conventional OLTP (On-line Transaction Processing) applications.OLAP applications require viewing the data from many different business perspectives (dimensions).Data cube [GBLP95] is a multidimensional view of a databases where a critical value, e.g., sales, is organized by several dimensions, for example, sales of automobiles organized by model, color, day of sale and so on. The metric of interest is called the measure attrzbute, which is sales in the example. It is generally accepted that OLAP systems need to present such a multidimensional view of the data to users. Each cell of the data cube corresponds to a unique set of values for the different dimensions and contains the value of the measure for this set of values. As mentioned in [GBLP95], the domain of each dimension is augmented with the special value "ALL." ln order to present this multidimensional view, the data is usually stored in the form of "summary tables" corresponding to the subcubes of the data cube.'In [HRU96] , the efficient implementation of "data *This work was supported by NSF grant IRI-92-23405, ARO gran...

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Abstract.A data warehouse stores materialized views derived from one or more sources for the purpose of efficiently implementing decisionsupport or OLAP queries. One of the most important decisions in designing a data warehouse is the selection of materialized views to be maintained at the warehouse. The goal is to select an appropriate set of views that minimizes total query response time and/or the cost of maintaining the selected views, given a limited amount of resource such as materialization time, storage space, or total view maintenance time. In this article, we develop algorithms to select a set of views to materialize in a data warehouse in order to minimize the total query response time under the constraint of a given total view maintenance time. As the above maintenance-cost view-selection problem is extremely intractable, we tackle some special cases and design approximation algorithms. First, we design an approximation greedy algorithm for the maintenance-cost view-selection problem in OR view graphs, which arise in many practical applications, e.g., data cubes. We prove that the query benefit of the solution delivered by the proposed greedy heuristic is within 63% of that of the optimal solution. Second, we also design an A * heuristic, that delivers an optimal solution, for the general case of AND-OR view graphs. We implemented our algorithms and a performance study of the algorithms shows that the proposed greedy algorithm for OR view graphs almost always delivers an optimal solution.

Micron-sized CaO, obtained by calcination of mesoporous CaCO3, attained 36 wt % CO2 sorption capacity after 100 cycles of carbonation and calcination reactions at 700 °C. The extent of simultaneous carbonation (X CO 2 ) and sulfation (X SO 2 ) of CaO at 700 °C was obtained under simulated flue gas conditions (10% CO2, 3000 ppm of SO2, 4% O2 in N2). CaO reacts with SO2 to form thermally stable CaSO4, which leads to a reduction in the CO2 capture capacity of CaO. Whereas X SO 2 increases monotonically with the residence time, X CO 2 goes through a maximum and eventually drops as a result of direct sulfation of CaCO3. The maximum value attained by X CO 2 was 50% in 10 min in the first cycle. The highest X CO 2 /X SO 2 ratio of 5 is attained at a residence time of 5 min. X SO 2 is higher under simultaneous carbonation and sulfation conditions compared to sulfation of CaO or direct sulfation of CaCO3.