An Efficient Concurrency Control Algorithm for High-Dimensional Index Structures

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.

When SIFT algorithm is applied to image feature matching on the mobile platform, it has been found some issues such as slow search rate, high memory consumption and frequency I/O, etc. Aiming at such problems, this paper studies on high dimensional data index structure for SIFT image feature matching, proposed a K-Dvd-tree index structure. This structure divides high dimensional data into multiple subspace using vantage dimension in order to increase the fan-out degree of the tree, controls the number of feature points in leaf node in order to reduce the height of the tree, and ultimately raise the search rate, at the same time, it stores multiple internal nodes at the same level within one page, and makes each leaf node correspond to one page in order to reduce memory consumption, and improve I/O efficiency as well. the experiments show that the performance of this index structure is relatively increased in terms of search rate and I/O efficiency.

The importance of multidimensional index structures to numerous emerging database applications is well established. However, before these index structures can be supported as access methods (AMs) in a “commercial-strength” database management system (DBMS), efficient techniques to provide transactional access to data via the index structure must be developed. Concurrent accesses to data via index structures introduce the problem of protecting ranges specified in the retrieval from phantom insertions and deletions (the phantom problem ). This paper presents a dynamic granular locking approach to phantom protection in Generalized Search Trees(GiSTs), an index structure supporting an extensible set of queries and data types. The granular locking technique offers a high degree of concurrency and has a low lock overhead. Our experiments show that the granular locking technique (1) scales well under various system loads and (2) similar to the B-tree case, provides a significantly more efficient implementation compared to predicate locking for multidimensional AMs as well. Since a wide variety of multidimensional index structures can be implemented using GiST, the developed algorithms provide a general solution to concurrency control in multidimensional AMs. To the best of our knowledge, this paper provides the first such solution based on granular locking.

With the rapid growth of database applications, wireless networking technology and mobile computing devices, there is a demand for processing mobile transactions in Mobile Ad-hoc Network (MANET) databases, so that mobile users can access and manipulate data anytime and anywhere. However, in order to guarantee timely and correct results for multiple concurrent transactions, concurrency control (CC) techniques become critical. Due to the characteristics of MANET databases, existing CC algorithms cannot work effectively. In this paper, we propose a CC algorithm called Sequential Order with Dynamic Adjustment (SODA) for MANET databases. In the design of SODA, the major characteristics of MANET databases are taken into consideration. SODA is based on optimistic CC to offer high concurrency and avoid unbounded blocking time, utilizes the sequential order of committed transactions to improve response time, and dynamically adjusts the sequential order of committed transactions to reduce aborts. The simulation results confirm that SODA has lower abort rate than other existing techniques.

Fast media search and browsing is today a growing need to face the challenge of managing large collections of personal media. Traditional databases, (e.g. MySQL), or text databases (e.g. Lucene), do not address an important aspect of multimedia data: the high-dimensionality of data. In this paper, we describe the implementation and evaluation of high-dimensional data indexing structures for fast search and browsing. Index structures for high-dimensional data have been researched in the literature and several proposals exist in the literature. We compare five popular index structures on a large-scale image retrieval scenario with different visual features of varying dimensionality. Both indexing and search aspects are evaluated: indexing time, search time and the trade-off between precision and performance.

It is required to research on high-dimensional index structures for efficiently retrieving high-dimensional data because an attribute vector in data warehousing and a feature vector in multimedia database have a characteristic of high-dimensional data. For this, many high-dimensional index structures have been proposed, but they have so called ‘dimensional curse’ problem that retrieval performance is extremely decreased as the dimensionality is increased. To solve the problem, the cell-based filtering (CBF) scheme has been proposed. But the CBF scheme show a linear decreasing on performance as the dimensionality. To cope with the problem, it is necessary to make use of parallel processing techniques. In this paper, we propose a parallel CBF scheme which uses a horizontally-partitioned technique as declustering. In order to maximize the retrieval performance of the proposed parallel CBF scheme, we construct our parallel CBF scheme under a SN (Shared Nothing) cluster architecture. In addition, we present a data insertion algorithm, a rage query processing one, and a k-NN query processing one which are suitable for the SN cluster architecture. Finally, we show that our parallel CBF scheme achieves better retrieval performance in proportion to the number of servers in the SN cluster architecture, compared with the conventional CBF scheme.

Hierarchical concurrency control has been proposed as one possible approach for multidatabase systems. However, to apply this approach, some restrictions must be imposed on the local concurrency control algorithms. The restriction is identified. Based on this restriction, the hierarchical concurrency control approach is formalized and its correctness is proven. A novel global concurrency control algorithm based on this hierarchical approach is also presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The vector approximation file (VA-file) approach is an efficient high-dimensional indexing method using compression technique to overcome the difficulty of 'curse of dimensionality'. The VA-file method combined with tree-based index structure can improve the querying efficiency, but it still succumbs to the 'curse of dimensionality'. In this paper, a new high-dimensional indexing structure called PCR-tree for non-uniform distributed data sets was presented, which employs R-tree to manage the approximate vectors in the reduced-dimensionality space. The approximate vectors can be built in the KL transform domain, and low dimensional MBRs (minimum bounding rectangles) can be used to manage the approximations on the first few principal components. When performing k-nearest neighbor search, a lower-bound filtering algorithm is used to reject the improper nodes of PCR-tree, which can reduce the computational complexity and I/O cost without any dismissals. The experiment results on large image databases show that the new approach provides a faster search speed than other tree-structured vector approximation approaches.

Computer architectures are moving towards an era dominated by many-core machines with dozens or even hundreds of cores on a single chip. This unprecedented level of on-chip parallelism introduces a new dimension to scalability that current database management systems (DBMSs) were not designed for. In particular, as the number of cores increases, the problem of concurrency control becomes extremely challenging. With hundreds of threads running in parallel, the complexity of coordinating competing accesses to data will likely diminish the gains from increased core counts. To better understand just how unprepared current DBMSs are for future CPU architectures, we performed an evaluation of concurrency control for on-line transaction processing (OLTP) workloads on many-core chips. We implemented seven concurrency control algorithms on a main-memory DBMS and using computer simulations scaled our system to 1024 cores. Our analysis shows that all algorithms fail to scale to this magnitude but for different reasons. In each case, we identify fundamental bottlenecks that are independent of the particular database implementation and argue that even state-of-the-art DBMSs suffer from these limitations. We conclude that rather than pursuing incremental solutions, many-core chips may require a completely redesigned DBMS architecture that is built from ground up and is tightly coupled with the hardware.

A new secure 2PL real-time concurrency control algorithm (ES2PL)

Majority of the research in multilevel secure database management systems (MLS/DBMS) focuses primarily on centralized database systems. However, with the demand for higher performance and higher availability, database systems have moved from centralized to distributed architectures, and the research in distributed database management systems (MLS/DDBMS) is gaining more and more prominence. Concurrency control is an integral part of database systems. Secure concurrency control algorithms [15,17,18,29,] proposed in literature achieve correctness and security at the cost of declined performance of high security level transactions. These algorithms infringe the fairness in processing transactions at different security levels. Though the performance of different concurrency control algorithms have been explored extensively for centralized multilevel secure database management systems [11,31] but to the best of author’s knowledge the relative performance of transactions at different security levels using secure concurrency control algorithm for has not been reported yet. To fill this gap, this paper presents a detailed simulation model of a multilevel secure distributed database system and investigates the performance price paid for maintaining security in a MLS/DDBMS. The paper investigates the relative performance of transactions at different security levels.

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.

Large knowledge bases that are intended for applications such as CAD, corporate repositories or process control will have to be shared by multiple users. For these systems to scale up, to give acceptable performance and to exhibit consistent behavior, it is mandatory to synchronize user transactions using a concurrency control algorithm. In this paper, we examine a novel concurrency control policy called Dynamic Directed Graph (or DDG) policy that effectively exploits the rich semantic structure of a knowledge base.

In this paper, we present a study on the concurrency control and recovery algorithms in nested transaction environment. We have reviewed the work done in the area of nested transaction modelling, its applications in object-oriented and mobile databases, and in workflow models. We have contrasted various nested transaction models by discussing their advantages and disadvantages. We have outlined some important future research directions in the area of nested transaction processing.

The partitioned synchronization rule is a technique for proving the correctness of concurrency control algorithms. Prior work has shown the applicability of the partitioned synchronization rule to hierarchically decomposed databases whose structure is restricted to semitrees. The principal contribution of the paper is a demonstration that the partitioned synchronization rule also applies to more general structures than semitrees, specifically, to any planar extendible partial order, a partial order which when extended with a least and a greatest element still remains planar. To demonstrate utility, the paper presents two applications of the partitioned synchronization rule. The first application shows correctness of a component based timestamp generation algorithm suitable for implementing a timestamp ordering concurrency control algorithm. The second application shows correctness of a snapshot algorithm for concurrency control in a replicated multilevel secure database; we choose this application to highlight that hierarchically decomposed databases and multilevel secure databases are structurally similar. In both cases, the correctness proofs via the partitioned synchronization rule are substantially simpler than corresponding direct proofs. >