Range updates and range sum queries on multidimensional points with monoid weights

Range sum queries on data cubes are a powerful tool for analysis. A range sum query applies an aggregation operation (e.g., SUM) over all selected cells in a data cube, where the selection is specified by providing ranges of values for numeric dimensions. Many application domains require that information provided by analysis tools be current or "near-current." Existing techniques for range sum queries on data cubes, however, can incur update costs on the order of the size of the data cube. Since the size of a data cube is exponential in the number of its dimensions, rebuilding the entire data cube can be very costly. We present an approach that achieves constant time range sum queries while constraining update costs. Our method reduces the overall complexity of the range sum problem.

I/O parallelism is considered to be a promising approach to achieving high performance in parallel data warehousing systems where huge amounts of data and complex analytical queries have to be processed. This paper proposes a parallel secondary data cube storage structure (PHC for short) to efficiently support the processing of range sum queries and dynamic updates on data cube using parallel computing systems. Based on PHC, two parallel algorithms for processing range sum queries and updates are proposed also. Both the algorithms have the same time complexity, O(logd n/P). The analytical and experimental results show that PHC and the parallel algorithms have high performance and achieve optimum speedup.

Given a set of positive-weighted points and a query rectangle r (specified by a client) of given extents, the goal of a maximizing range sum (MaxRS) query is to find the optimal location of r such that the total weights of all the points covered by r are maximized. All existing methods for processing MaxRS queries assume the Euclidean distance metric. In many location-based applications, however, the motion of a client may be constrained by an underlying (spatial) road network; that is, the client cannot move freely in space. This paper addresses the problem of processing MaxRS queries in a road network. We propose the external-memory algorithm that is suited for a large road network database. In addition, in contrast to the existing methods, which retrieve only one optimal location, our proposed algorithm retrieves all the possible optimal locations. Through simulations, we evaluate the performance of the proposed algorithm.

This paper is about privacy preservation of the data in OLAP data cube. Data cube is a multidimensional view of a database, which helps in analysis of data from different perspectives. Preserving privacy of individual's data while providing all data available for the analysis is one of the main challenges for OLAP systems. Because legitimate queries on aggregated values lead to the inference of individual's data or sensitive data. We propose a data perturbation technique called uniformly adjusted distortion, which initially distorts one cell and then uniformly distributes this distortion in the whole data cube. This uniform distribution not only preserves the aggregates but also provides maximum accuracy with range sum queries and high availability.

This work presents a distributed implementation for processing Maximizing Range Sum (MaxRS) query in Wireless Sensor Networks (WSN). MaxRS query is useful in many spatially-distributed event monitoring and target tracking applications. Given the location and current readings of the nodes, and a rectangle R, MaxRS finds a location of R that maximizes the sum of the readings of all the nodes covered by R. Our system performs MaxRS query in a user-specified time-interval γ and using the result obtained, attempts to maintain a certain degree of energy conservation in the WSN, based on a user-defined threshold δ. Since centralized processing of the raw readings and subsequently determining the MaxRS may incur significant communication overheads, we developed a distributed algorithm to compute MaxRS. We implemented our system in a heterogeneous WSN consisting of TelosB and SunSPOT motes, and illustrate the end-user tools: GUI for specifying required parameters, and real-time visualization of MaxRS solutions and estimated network energy consumption.

A range query applies an aggregation operation over all selected cells of an OLAP data cube where selection is specified by the range of contiguous values for each dimension. Many works have focused on efficiently computing range sum or range max queries. Most of these algorithms use a uniformly partitioning scheme for the data cube. In this paper, we improve on query costs of some of these existing algorithms by noting two key areas. First, end-user range queries usually involve repetitive query patterns, which provide a variable sized partitioning scheme that can be used to partition the data cubes. Query costs are reduced because pre-computation is retrieved for entire partitions, rather than computed for a partial region in many partitions, which requires large amounts of cell accesses to the data cube. Second, data in the data cube can be arranged such that each partition is stored in as few physical storage blocks as possible, thus reducing the I/O costs for answering range queries.

Approximate range aggregate queries are one of the most frequent and useful kinds of queries for Decision Support Systems (DSS), as they are widely used in many data analysis tasks. Traditionally, sampling-based techniques have been proposed to tackle this problem. However, their effectiveness degrade when the underlying data distribution is skewed. Another approach based on the outlier management can limit the effect of data skews but fails to address other requirements of approximate range aggregate queries, such as error guarantees and query processing efficiency. In this paper, we present a technique that provides approximate answers to range aggregate queries on OLAP data cubes efficiently, with theoretical guarantees on the errors. Our basic idea is to build different data structures to manage outliers and the rest of the data. Carefully chosen outliers are organized in a quad-tree based indexing data structure to provide efficient access for query processing. A query-workload adaptive, tree-like synopsis data structure, called T unable P artition-Tree (TP-Tree), is proposed to organize samples extracted from non-outlier data. Our experiments clearly demonstrate the merits of our technique, by comparing with previous well-known techniques.

Many optimization problems encountered in practice are multi-objective by nature, i.e., different objectives are conflicting and equally important. Many times, it is not desirable to drop some of them or to optimize them in a composite single objective or hierarchical manner. Furthermore, cost parameters change over time which makes optimization problems harder. For instance, in the transport sector, travel costs are a function of travel time which changes depending on the time of the day a vehicle is travelling (e.g., due to road congestion). Road congestion results in tremendous delays which lead to a decrease in the service quality and the responsiveness of logistic service providers. In Chapter 2, we develop a generic approach to deal with Multi-Objective Scheduling Problems (MOSPs) with State-Dependent Cost Parameters. The aim is to determine the set of Pareto solutions that capture the trade offs between the different conflicting objectives. Due to the complexity of MOSPs, an efficient approximation based on dynamic programming is developed. The approximation has a provable worse case performance guarantee. Even though the generated approximate Pareto front consist of fewer solutions, it still represents a good coverage of the true Pareto front. Furthermore, considerable gains in computation times are achieved. In Chapter 3, the developed methodology is validated on the multi-objective timedependent knapsack problem. In the classical knapsack problem, the input consists of a knapsack with a finite capacity and a set of items, each with a certain weight and a cost. A feasible solution to the knapsack problem is a selection of items such that their total weight does not exceed the knapsack capacity. The goal is to maximize the single objective function consisting of the total pro t of the selected items. We extend the classical knapsack problem in two ways. First, we consider time-dependent profits (e.g., in a retail environment profit depends on whether it is Christmas or not).