37917309
Management of Concurrency in Interpolation Based Grid File Organization and its Performance
The mathematical structure of the interpolation based grid file organization provides a suitable platform for supporting a high degree of concurrency under a concurrency control mechanism. To demonstrate this capability, we first identified its useful properties for designing a concurrency control mechanism and then developed a deadlock-free two-phase locking based mechanism using dynamic attributes of transactions. To establish the usefulness of our system (concurrency control and the file organization), we measured its performance through simulation modeling. We show that our system handles a number of different types of workloads efficiently and the directory overhead does not strictly depend on the type of workload.
34436430
An Integrated Data Structure with Multiple Access Paths for Database Systems and its Performance
Abstract In the past a number of file organizations have been proposed for processing different types of queries efficiently. To our knowledge none of the existing file organizations is capable of supporting all types of accesses equally efficiently. In this paper we have taken a different approach for designing an integated data structure which offers multiple access paths for processing different types of queries efficiently. The data structure reported here can be implemented on disk based as well as main memory database systems, however, in this paper we report its behavior mainly in main memory database environment. Our approach is to fuse those data structures which offer an efficient access paths for a particular type. To show the feasibility of our scheme we fused the B+-tree, the grid file and extendible hashing structures, using a proper interface. We implemented and measured its performance through simulation modeling. Our results show that the data structure does improve concurrency and offers a higher throughput for a variety of transaction processing workloads. We argue that our scheme is different than creating secondary indexes for improving concurrency. In the absence of a data strucure which can provide all types of access equally efficiently, an integrated data structure is an acceptable solution which offers an efficient way for increasing the performance of database management systems.
29393244
Semantic Caching and Query Processing
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.
35078097
An adaptive location management algorithm for mobile computing
In mobile computing mobile hosts or mobile units move randomly from one place to another within a well-defined geographical area. This requires some mechanism for identifying the exact location of a mobile host. Such mechanisms are usually referred to as location management and is one of the essential components for supporting mobility. In the past a number of efficient location management schemes have been suggested but they lack adaptability. We believe that an adaptable location management scheme is more desirable because of frequent change in the location of mobile hosts. In this paper, we report a location management algorithm, which adapts to the constant changing scenario. Our algorithm makes use of some of the concepts of location management schemes proposed in the past.
16958826
Recent Advances and Research Problems in Data Warehousing
In the recent years, the database community has witnessed the emergence of a new technology, namely data warehousing. A data warehouse is a global repository that stores pre-processed queries on data which resides in multiple, possibly heterogeneous, operational or legacy sources. The information stored in the data warehouse can be easily and efficiently accessed for making effective decisions. The On-Line Analytical Processing (OLAP) tools access data from the data warehouse for complex data analysis, such as multidimensional data analysis, and decision support activities. Current research has lead to new developments in all aspects of data warehousing, however, there are still a number of problems that need to be solved for making data warehousing effective. In this paper, we discuss recent developments in data warehouse modelling, view maintenance, and parallel query processing. A number of technical issues for exploratory research are presented and possible solutions are discussed.
8261257
TCOT-A Timeout-Based Mobile Transaction Commitment Protocol
We present a transaction commit protocol, "Transaction Commit On Timeout (TCOT)," based on a "timeout" approach for Mobile Database Systems (MDS), which can be universally used to reach a final transaction termination decision (e.g., commit, abort, etc.) in any message-oriented system. Particularly suited for a wireless environment, a timeout mechanism is the only way to minimize the impact of the slow and unreliable wireless link. We compare TCOT to a modified version of 2PC to show its superiority based on commit time.
11619174
Provable algorithms for parallel sweep scheduling on unstructured meshes
We present provably efficient parallel algorithms for sweep scheduling on unstructured meshes. Sweep scheduling is a commonly used technique in radiation transport problems, and involves inverting an operator by iteratively sweeping across a mesh. Each sweep involves solving the operator locally at each cell. However, each direction induces a partial order in which this computation can proceed. On a distributed computing system, the goal is to schedule the computation, so that the length of the schedule is minimized. Several heuristics have been proposed for this problem; but none of the heuristics have worst case performance guarantees. We present a simple, almost linear time randomized algorithm which (provably) gives a schedule of length at most O(log/sup 2/n) times the optimal schedule for instances with n cells, when the communication cost is not considered, and a slight variant, which coupled with a much more careful analysis, gives a schedule of (expected) length O(logmlogloglogm) times the optimal schedule for m processors. These are the first such provable guarantees for this problem. We also design a priority based list schedule using these ideas, with the same theoretical guarantee, but much better performance in practice. We complement our theoretical results with extensive empirical analysis. The results show that (i) our algorithm performs very well and has significantly better performance guarantee in practice and (ii) the algorithm compares favorably with other natural and efficient parallel algorithms proposed in the literature [S. Pautz, (2002), S. Plimpton et al., (2001)].
26507710
Graph Partitioning for Dynamic, Adaptive, and Multi-phase Computations
Algorithms that find good partitionings of highly unstructured graphs are critical in developing efficient algorithms for problems in a variety of domains such as scientific simulations that require solution to large sparse linear systems, VLSI design, and data mining. Even though this problem is NP-hard, efficient multi-lev el algorithms have been developed that can find good partitionings of static irregular meshes. The problem of graph partitioning becomes a lot more challenging when the graph is dynamically evolving (e.g., in adaptive computations), or if computation in multiple phases needs to be balanced simultaneously. This talk will discuss these challenges, and then describe some of our recent research in addressing them.
15738427
A potential field based approach to multi-robot manipulation
We describe a framework for controlling and coordinating a group of robots for cooperative manipulation tasks. The framework enables a decentralized approach to planning and control. It allows the robots to approach the object, organize themselves into a formation that will trap the object, and then transport the object to the desired destination. Our controllers and planners are derived from simple potential fields and the hierarchical composition of potential fields. We show how these potential field based controllers and planners benefit complex group interactions, specifically for manipulating and transporting objects in the plane. Theoretically, we show how we can derive results on formation stability with potential field based controllers in many cases. Simulation results demonstrate successful application to a wide range of examples without showing sensitivity to parameters. Because the framework is decentralized at both trajectory generation level and the estimation and control agent level, our framework can potentially scale to groups of tens and hundreds of robots.
12800836
Computational Techniques for Analysis of Genetic Network Dynamics
In this paper we propose modeling and analysis techniques for genetic networks that provide biologists with insight into the dynamics of such systems. Central to our modeling approach is the framework of hybrid systems and our analysis tools are derived from formal analysis of such systems. Given a set of states characterizing a property of biological interest P , we present the Multi-Affine Rectangular Partition (MARP) algorithm for the construction of a set of infeasible states I that will never reach P and the Rapidly Exploring Random Forest of Trees (RRFT) algorithm for the construction of a set of feasible states F that will reach P. These techniques are scalable to high dimensions and can incorporate uncertainty (partial knowledge of kinetic parameters and state uncertainty).We apply these methods to understand the genetic interactions involved in the phenomenon of luminescence production in the marine bacterium V. fischeri.
38254367
Parallel formulations of decision-tree classification algorithms
Classification decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud detection, etc. Highly parallel algorithms for constructing classification decision trees are desirable for dealing with large data sets in reasonable amount of time. Algorithms for building classification decision trees have a natural concurrency, but are difficult to parallelize due to the inherent dynamic nature of the computation. We present parallel formulations of classification decision tree learning algorithm based on induction. We describe two basic parallel formulations. One is based on Synchronous Tree Construction Approach and the other is based on Partitioned Tree Construction Approach. We discuss the advantages and disadvantages of using these methods and propose a hybrid method that employs the good features of these methods. Experimental results on an IBM SP-2 demonstrate excellent speedups and scalability.