Balakrishna R. Iyer

Data cube approximation and histograms via wavelets

There has recently been an explosion of interest in the analysis of data in data warehouses in the eld of On-Line Analytical Processing (OLAP). Data warehouses can be extremely large, yet obtaining quick answers to queries is important. In many situations, obtaining the exact answer to an OLAP query is prohibitively expensive in terms of time and/or storage space. It can be advantageous to have fast, approximate answers to queries. In this paper, we present an I/O-eecient technique based upon a multiresolution wavelet decomposition that yields an approximate and space-eecient representation of the data cube, which is one of the core OLAP operators. We build our compact data cube on the logarithms of the partial sums of the raw data values of a multidimensional array. We get excellent approximations for on-line range-sum queries with limited space usage and computational cost. Multiple data cubes can be handled simultaneously. Each query can generally be answered, depending upon the accuracy supported, in one I/O or a small number of I/Os. Experiments show that our method performs signiicantly better than other approximation techniques such as histograms and random sampling.

Efficient Execution of Aggregation Queries over Encrypted Relational Databases

Encryption is a common method to assure privacy of stored data. In many practical situations, decrypting data before applying logic compromises privacy. The challenge is to come up with logic transformation techniques and result-mapping methods so that the exact result of applying logic to data-in-the-clear is obtained by applying the transformed logic to encrypted data and mapping the result produced. In the scope of relational aggregation queries and in the presence of logical predicates, we show how to support needed transformations and mappings.

Analysis of a composite performance reliability measure for fault-tolerant systems

Todays concomitant needs for higher computing power and reliability has increased the relevance of multiple-processor fault-tolerant systems. Multiple functional units improve the raw performance (throughput, response time, etc.) of the system, and, as units fail, the system may continue to function albeit with degraded performance. Such systems and other fault-tolerant systems are not adequately characterized by separate performance and reliability measures. A composite measure for the performance and reliability of a fault-tolerant system observed over a finite mission time is analyzed. A Markov chain model is used for system state-space representation, and transient analysis is performed to obtain closed-form solutions for the density and moments of the composite measure. Only failures that cannot be repaired until the end of the mission are modeled. The time spent in a specific system configuration is assumed to be large enough to permit the use of a hierarchical model and static measures to quantify the performance of the system in individual configurations. For a multiple-processor system, where performance measures are usually associated with and aggregated over many jobs, this is tantamount to assuming that the time to process a job is much smaller than the time between failures. An extension of the results to general acyclic Markov chain models is included.

On coupling multi-systems through data sharing

The demand for larger transaction rates and the inability of single-system-based transaction processors to keep up with demand have resulted in the growth of multi-processor-based database systems. The focus here is on coupling in a locally distributed system through multi-system data sharing in which all systems have direct access to the data. This paper addresses the following questions; i) How does a workload running on a single system today perform if migrated to a multi-system? ii) What are the multi-system locking design issues that limit multi-system performance and what is the maximum number of systems that may be effectively coupled? iii) Can alternative locking designs increase the number of systems that may be effectively coupled? Our analysis is based on traces from large mainframe systems running IBMs IMS database management system. We have developed a hierarchical modeling methodology that starts by synthesizing a multi-system IMS lock trace and a reference trace from single-system traces. The multisystem traces are used in trace-driven simulations to predict lock contention and database I/O increase in multi-system environment and to generate workload parameters. These parameters are used in event-driven simulation models to examine the overall performance under different system structures. Performance results are presented for realistic system parameters to determine the performance impact of various design parameters. Lock contention is found to be the critical factor in determining the coupling effectiveness and the effect of alternative locking design to reduce lock contention is studied. The limit on coupling is explored and the analysis indicates that, for this workload, on the order of 6 to 12 systems may be effectively coupled through data sharing, depending on system structure and locking design.

Query optimization in encrypted database systems

To ensure the privacy of data in the relational databases, prior work has given techniques to support data encryption and execute SQL queries over the encrypted data. However, the problem of how to put these techniques together in an optimum manner was not addressed, which is equivalent to having an RDBMS without a query optimizer. This paper models and solves that optimization problem.

A Framework for Efficient Storage Security in RDBMS

With the widespread use of e-business coupled with the public’s awareness of data privacy issues and recent database security related legislations, incorporating security features into modern database products has become an increasingly important topic. Several database vendors already offer integrated solutions that provide data privacy within existing products. However, treating security and privacy issues as an afterthought often results in inefficient implementations. Some notable RDBMS storage models (such as the N-ary Storage Model) suffer from this problem. In this work, we analyze issues in storage security and discuss a number of trade-offs between security and efficiency. We then propose a new secure storage model and a key management architecture which enable efficient cryptographic operations while maintaining a very high level of security. We also assess the performance of our proposed model by experimenting with a prototype implementation based on the well-known TPC-H data set.

Estimating alphanumeric selectivity in the presence of wildcards

Success of commercial query optimizers and database management systems (object-oriented or relational) depend on accurate cost estimation of various query reordering [BGI]. Estimating predicate selectivity, or the fraction of rows in a database that satisfy a selection predicate, is key to determining the optimal join order. Previous work has concentrated on estimating selectivity for numeric fields [ASW, HaSa, IoP, LNS, SAC, WVT]. With the popularity of textual data being stored in databases, it has become important to estimate selectivity accurately for alphanumeric fields. A particularly problematic predicate used against alphanumeric fields is the SQL like predicate [Dat]. Techniques used for estimating numeric selectivity are not suited for estimating alphanumeric selectivity.In this paper, we study for the first time the problem of estimating alphanumeric selectivity in the presence of wildcards. Based on the intuition that the model built by a data compressor on an input text encapsulates information about common substrings in the text, we develop a technique based on the suffix tree data structure to estimate alphanumeric selectivity. In a statistics generation pass over the database, we construct a compact suffix tree-based structure from the columns of the database. We then look at three families of methods that utilize this structure to estimate selectivity during query plan costing, when a query with predicates on alphanumeric attributes contains wildcards in the predicate.We evaluate our methods empirically in the context of the TPC-D benchmark. We study our methods experimentally against a variety of query patterns and identify five techniques that hold promise.

Integrated concurrency-coherency controls for multisystem data sharing

The authors propose an integrated control mechanism and analyze the performance gain due to its use. An extension to the data sharing system structure is examined in which a shared intermediate memory is used for buffering and for early commit processing. Read-write-synchronization and write-serialization problems arise. The authors show how the integrated concurrency protocol can be used to overcome both problems. A queueing model is used to quantify the performance improvement. Although using intermediate memory as a buffering device produces a moderate performance benefit, the analysis shows that more substantial gains can be realized when this technique is combined with the use of an integrated concurrency-coherency control protocol. >

A polynomial time algorithm for optimizing join queries

The dynamic programming algorithm for query optimization has exponential complexity. An alternative polynomial time algorithm, the IK-KBZ algorithm, is severely limited in the queries it can optimize. Other algorithms have been proposed, including the greedy algorithm, iterative improvement, and simulated annealing. The AB algorithm, which combines randomization and neighborhood search with the IK-KBZ algorithm, is presented. The AB algorithm is much more generally applicable than IK-KBZ, has polynomial time and space complexity, and produces near optimal plans in the space of outer linear join trees. On average, it does better than the other algorithms that do not do an exhaustive search like dynamic programming.<<ETX>>

An efficient hybrid join algorithm: a DB2 prototype

A new join method, called hybrid join, is proposed which uses the join-index filtering and the skip sequential prefetch mechanism for efficient data access. With this method, the outer table is sorted on the join column. Then, the outer is joined with the index on the join column of the inner. The inner tuple is represented by its surrogate, equivalent of its physical disk address, which is carried in the index. The partial join result is sorted on the surrogate and then the inner table is accessed sequentially to complete the join result. Local predicate filtering can also be applied before the access of the inner relation through the index AND/ORing. Efficient methods for skip sequential access and prefetching of logically discontiguous leaf pages of B/sup +/-tree indexes are also presented.<<ETX>>