A Database Approach to Communication in VLSI Design

Current database systems utilize histograms to approximate frequency distributions of attribute values of relations. These are used to efficiently estimate query result sizes and access plan costs and thus minimize the query response time for business and (non commercial) database systems. In two recent works (Oommen, Thiyagarajah 1999a, Oommen, Thiyagarajah 1999b) we proposed two new forms of histogramlike techniques called the Rectangular and Trapezoidal Attribute Cardinality Maps (ACM) respectively. Since these techniques are based on the philosophies of numerical integration, they provide much more accurate result size estimations than the traditional equi-width and equi-depth histograms currently being used by many commercial database systems. In (Oommen, Thiyagarajah 1999a; Oommen, Thiyagarajah 1999b) we also provided a fairly extensive mathematical analysis for their average and worst case errors for their frequency estimates—which, in turn, were verified for synthetic data. This paper reports the prototype validation for the Rectangular-ACM (R-ACM) for query optimization in real-world database systems. By using an extensive set of experiments using real-life data (U.S. Census 1997, NBA 1992), we demonstrate that the T-ACM scheme is much more accurate than the traditional histograms for query result size estimation. We anticipate that it could become an invaluable tool for query optimization in the future.Key wordsQuery OptimizationQuery Result Size Estimation

Modern database systems integrate diverse technology to implement data security. One important aspect of the security scheme is about using encryption to protect sensitive information while it remains stored in the database. We first review in this paper the general aspects of data security and analyze how the leading commercial databases address these issues. Then we discuss about applying encryption as a justified measure of increasing the data privacy and we identify a weak support from commercial database systems when a group of users owns encrypted data. Finally, we propose an enhanced mechanism, called secure groups, that intends to improve the management of encryption at a database level

The Imagine stream processor is a 21 million transistor chip implemented by a collaboration between Stanford University and Texas Instruments in a 1.5 V 0.15 /spl mu/m process with five layers of aluminum metal. The VLSI design, clocking, and verification methodologies for the Imagine processor are presented. These methodologies enabled a small team of graduate students with limited resources to design a high-performance media processor in a modern ASIC flow.

Concurrency control is the activity of synchronizing operations issued by concurrent executing transactions on a shared database. The aim of this control is to provide an execution that has the same effect as a serial (non-interleaved) one. The optimistic concurrency control technique allows the transactions to execute without synchronization, relying on commit-time validation to ensure serializability. Effectiveness of the optimistic techniques depends on the conflict rate of transactions. Since different systems have various patterns of conflict and the patterns may also change over time, so applying the optimistic scheme to the entire system results in degradation of performance. In this paper, a novel algorithm is proposed that dynamically selects the optimistic or pessimistic approach based on the value of conflict rate. The proposed algorithm uses an adaptive resonance theory–based neural network in making decision for granting a lock or detection of the winner transaction. In addition, the parameters of this neural network are optimized by a modified gravitational search algorithm. On the other hand, in the real operational environments we know the writeset (WS) and readset (RS) only for a fraction of transactions set before execution. So, the proposed algorithm is designed based on optional knowledge about WS and RS of transactions. Experimental results show that the proposed hybrid concurrency control algorithm results in more than 35 % reduction in the number of aborts in high-transaction rates as compared to strict two-phase locking algorithm that is used in many commercial database systems. This improvement is 13 % as compared to pure-pessimistic approach and is more than 31 % as compared to pure-optimistic approach.

The histogram is the most common method for size estimation of query results in many commercial database systems. Several histogram methods presented in the past have certain limitations in practicability due to their lack of guarantee of estimation accuracy. This paper presents two kinds of new histogram methods, which are easy to use and can guarantee the accuracy of the estimation. The influence of different data distributions on the histogram is discussed in the paper, and some important parameters of data distribution are introduced to help produce better histograms.

While the introduction of temporal extensions into database standards has proven difficult to achieve, the newly introduced SQL:2003 and XML/XQuery standards have actually enhanced our ability to support temporal applications in commercial database systems. We illustrate this point by discussing three approaches that use temporally grouped representations. We first compare the approaches at the logical level using a common set of queries; then we turn to the physical level and discuss our ArchIS system that supports the three different approaches efficiently in one unified physical implementation. We conclude that the approaches of managing transaction-time information using XML and SQL can be integrated and supported efficiently within the current standards, and claim that the proposed approach can be extended to valid-time and bitemporal databases.

Developing a commercial or specialized database system is an exceedingly costly and time consuming undertaking. A goal of this research is to demonstrate that a significant portion of a DBMS's software, specifically the physical database component, can be developed automatically from a small set of specifications.In this paper, we explain a new method of modeling physical databases and show that it provides a framework for realizing such a software development technology. Unlike any existing method, ours makes conceptual-to-internal mappings explicit. This enables the physical database component of operational DBMSs to be described in a systematic, precise, and simple way. Because our method also extends earlier research, it also provides a means for tieing physical database theory to practice.

Concurrency control (CC) algorithms guarantee the correctness and consistency criteria for concurrent execution of a set of transactions in a database. A precondition that is seen in many CC algorithms is that the writeset (WS) and readset (RS) of transactions should be known before the transaction execution. However, in real operational environments, we know the WS and RS only for a fraction of transaction set before execution. However, optional knowledge about WS and RS of transactions is one of the advantages of the proposed CC algorithm in this paper. If the WS and RS are known before the transaction execution, the proposed algorithm will use them to improve the concurrency and performance. On the other hand, the concurrency control algorithms often use a specific static or dynamic equation in making decision about granting a lock or detection of the winner transaction. The proposed algorithm in this paper uses an adaptive resonance theory (ART)-based neural network for such a decision making. In this way, a parameter called health factor (HF) is defined for transactions that is used for comparing the transactions and detecting the winner one in accessing the database objects. HF is calculated using ART2 neural network. Experimental results show that the proposed neural-based CC (NCC) algorithm increases the level of concurrency by decreasing the number of aborts. The performance of proposed algorithm is compared with strict two-phase locking (S2PL) algorithm, which has been used in most commercial database systems. Simulation results show that the performance of proposed NCC algorithm, in terms of number of aborts, is better than S2PL algorithm in different transaction rates.

The skyline operator was first proposed in 2001 for retrieving interesting tuples from a dataset. Since then, 100+ skyline-related papers have been published; however, we discovered that one of the most intuitive and practical type of skyline queries, namely, group-by skyline queries remains unaddressed. Group-by skyline queries find the skyline for each group of tuples. In this paper, we present a comprehensive study on processing group-by skyline queries in the context of relational engines. Specifically, we examine the composition of a query plan for a group-by skyline query and develop the missing cost model for the BBS algorithm. Experimental results show that our techniques are able to devise the best query plans for a variety of group-by skyline queries. Our focus is on algorithms that can be directly implemented in today's commercial database systems without the addition of new access methods (which would require addressing the associated challenges of maintenance with updates, concurrency control, etc.).

Join ordering and query optimization are crucial for query performance but remain challenging due to unknown or changing characteristics of query intermediates, especially for complex queries with many joins. Over the past two decades, a spectrum of techniques for adaptive query processing (AQP)---including inter-/intra-operator adaptivity and tuple routing---have been proposed to address these challenges. However, commercial database systems in practice do not implement holistic AQP techniques because they increase the system complexity (e.g., intertwined planning and execution) and thus, complicate debugging and testing. Additionally, existing approaches may incur large overheads, leading to problematic performance regressions. In this paper, we introduce POLAR, a simple yet very effective technique for a self-regulating selection of alternative join orderings with bounded overhead. We enhance left-deep join pipelines with alternative join orders, perform regret-bounded tuple routing to find and validate "plans of least resistance", and then process the majority of tuple batches through these plans. We study different join order selection techniques, different routing strategies, and a variety of workload characteristics. Our experiments with a POLAR prototype in DuckDB show runtime improvements of up to 9x and less than 7% overhead for all benchmark queries, while outperforming state-of-the-art AQP systems by up to 15x.

Recent years, the log-structured merge-tree(LSM-tree) style storage has been widely adopted in distributed data storage systems(e.g. Bigtable and HBase) and commercial database systems(e.g. Ocean-Base, Cassandra, SQLite, etc.) to provide both large-volume storage capacity and high-performance data updates. Write operations become easier as the LSM-tree style storage avoids writing in place by updating a data copy in memory. However, read operations are affected as it requires an additional step during a data compaction to check if there exists the newest update of data record in memory, which brings many of costly empty reads in real usage since the volume of immutable data is pervasively far more massive than incremental delta data. To address this issue, we design a new network request processing mechanism to allow data access being processed in an auxiliary lightweight network communication IO thread. And a Bloom filter is incorporated with the network IO thread to effectively filter out the empty reads. We also analyze the efficiency advantage of the mechanism and introduce its detailed implementation based on the well-known OceanBase system. Experimental study using the YCSB benchmark demonstrates the proposed mechanism can significantly achieve 20 percent to 30 percent better performance than existing method.

Article Free Access Share on Processing queries for first-few answers Authors: Roberto J. Bayardo Department of Computer Sciences and Applied Research Laboratories, The University of Texas at Austin Department of Computer Sciences and Applied Research Laboratories, The University of Texas at AustinView Profile , Daniel P. Miranker Department of Computer Sciences and Applied Research Laboratories, The University of Texas at Austin Department of Computer Sciences and Applied Research Laboratories, The University of Texas at AustinView Profile Authors Info & Claims CIKM '96: Proceedings of the fifth international conference on Information and knowledge managementNovember 1996 Pages 45–52https://doi.org/10.1145/238355.238372Online:12 November 1996Publication History 24citation256DownloadsMetricsTotal Citations24Total Downloads256Last 12 Months4Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Developing a commercial or specialized database system is an exceedingly costly and time consuming undertaking. A goal of this research is to demonstrate that a significant portion of a DBMS's software, specifically the physical database component, can be developed automatically from a small set of specifications.In this paper, we explain a new method of modeling physical databases and show that it provides a framework for realizing such a software development technology. Unlike any existing method, ours makes conceptual-to-internal mappings explicit. This enables the physical database component of operational DBMSs to be described in a systematic, precise, and simple way. Because our method also extends earlier research, it also provides a means for tieing physical database theory to practice.

Approximation is a very effective paradigm to speed up query processing in large databases. One popular approximation mechanism is data size reduction. There are three reduction techniques: sampling, histograms, and wavelets. Histogram techniques are supported by many commercial database systems, and have been shown very effective for approximately processing aggregation queries. In this paper, we will investigate the optimal models for building histograms based on linear spline techniques. We will firstly propose several novel models. Secondly, we will present efficient algorithms to achieve these proposed optimal models. Our experiment results showed that our new techniques can greatly improve the approximation accuracy comparing to the existing techniques.

A model on the role of noise at the neuronal and the cognitive levels