Basit Raza

Autonomic Success in Database Management Systems

One of the primary uses of computer is to reduce cost and manage complexity with increase in efficiency and performance. Now system complexity is reaching a level that is beyond human ability. With the development of technology, people want to manage complex systems in an efficient and reliable manner. Development of raw computing power and proliferation of computer devices and usage of internet has grown up to exponential rates. This growth and unprecedented levels of complexity is leading towards new direction - Autonomic Computing. Autonomic features in system increase speed, efficiency, reliability and accuracy with less or no human interaction, ultimately providing error free environment. These autonomic capabilities are important in Database Management Systems (DBMSs). The DBMSs which have the capability to manage and maintain themselves are called Autonomic Database Management Systems (ADBMS). The ADBMSs are evolving from last many years. At present most of the activities in DBMS are performed autonomically and have achieved certain level of autonomicity. The paper identified some autonomic shortcomings in commercial DBMSs up to 2002. We made a survey on achievements of autonomic computing against these shortcomings in current DBMSs. For this purpose, we have studied and analyzed IBM DB2, Oracle and Microsoft SQL Server.

Community Detection in Networks using Node Attributes and Modularity

Community detection in network is of vital importance to find cohesive subgroups. Node attributes can improve the accuracy of community detection when combined with link information in a graph. Community detection using node attributes has not been investigated in detail. To explore the aforementioned idea, we have adopted an approach by modifying the Louvain algorithm. We have proposed Louvain-AND-Attribute (LAA) and Louvain-OR-Attribute (LOA) methods to analyze the effect of using node attributes with modularity. We compared this approach with existing community detection approaches using different datasets. We found the performance of both algorithms better than Newman’s Eigenvector method in achieving modularity and relatively good results of gain in modularity in LAA than LOA. We used density, internal and external edge density for the evaluation of quality of detected communities. LOA provided highly dense partitions in the network as compared to Louvain and Eigenvector algorithms and close values to Clauset. Moreover, LOA achieved few numbers of edges between communities.

Autonomic View of Query Optimizers in Database Management Systems

The growing complexity of applications, huge data volume and the data structures to process massive data are becoming challenging issue. Query optimizer is a major component of a Database Management System (DBMS) that executes queries through different strategies and techniques efficiently. These techniques select the best optimal execution plan from the candidate plans according to the available resources and environment. Traditionally, skilled database administrators are required to tune DBMS for efficient query processing. Recently it has been realized to develop DBMSs having autonomic capabilities. Autonomic DBMS (ADBMS) are now being developed to reduce this dependency on an expensive skilled human resource. The paper analyzes the autonomic capabilities of query optimizers in three well-known DBMSs – DB2, Oracle and SQL Server being used in the industry. The research is focused to find and earmark those areas in query optimizers where the human intervention is required. Query Optimizers are compares with their autonomic capabilities, explores their strengths and weaknesses, and provides the basis for improving the current state of autonomic computing in query optimizers. The autonomic behavior of query optimizers is observed by designing and executing different queries through experiments and some recommendations are given.

Autonomicity in Universal Database DB2

Functionality, complexity, heterogeneity and dynamism in computing environment are increasing day by day. This enhanced utility of computers has a profound impact on the system’s brittleness, manageability and security. Self-management is important in systems, networks, communication as well as in Database Management Systems (DBMSs). Autonomic computing reduces the problems and increases accuracy and efficiency of the DBMSs. Recent years have seen an upsurge in research related to incorporation of autonomic computing within the computing systems. IBM is working on autonomic computing in systems and DBMSs from last many years. The paper [1] discussed the autonomic features and tools in DB2 from the literature available up to 2002. Our paper presents an extension of that paper which covers available autonomic features, components, utilities and tools up till now. A comparison of DB2 with Oracle is presented w.r.t autonomic computing. We have also identified the optional and essential human intervention (HI) in autonomic components, utilities and tools of IBM Universal Database DB2 that reveals the degree of autonomic computing in these.

Self-Prediction of Performance Metrics for the Database Management System Workload

Workload in Database Management System (DBMS) consists of huge amount of data and number of concurrent users who are executing different requests that require some resources. To manage these types of activities, organizations hire different database experts. There is versatility in workload due to the huge data size and different types of requests (workload). These factors contribute to some new challenges in the workload management. These challenges are identification of the workload and decision about the problem queries, identification of resource oriented and contention queries, accurate workload classification, optimal plan selection, prediction and adoption. In DBMS, where workload management and tuning is performed through if-then approach, unforeseen behavior of the workload cannot be handled and sometime leads to unpredictable state. In this research a prediction framework has been proposed called as workload queries performance Predictor. The predictor will predict the performance metrics (workload size, elapsed time, record accessed, record used, disk I/Os, memory required, message count and bytes) for queries in a given workload. We are improving efficiency and reducing search time when projection of query feature vector is performed over performance feature vector. The predictor will take help from the optimizer and store the information in database which saves the information as history for the future.

Autonomicity in Oracle Database Management System

Human world is becoming more and more dependent on computers and information technology (IT). The autonomic capabilities in computers and IT have become the need of the day. These capabilities in software and systems increase performance, accuracy, availability and reliability with less or no human intervention (HI). Database has become the integral part of information system in most of the organizations. Databases are growing w.r.t size, functionality, heterogeneity and due to this their manageability needs more attention. Autonomic capabilities in Database Management Systems (DBMSs) are also essential for ease of management, cost of maintenance and hide the low level complexities from end users. With autonomic capabilities administrators can perform higher-level tasks. The DBMS that has the ability to manage itself according to the environment and resources without any human intervention is known as Autonomic DBMS (ADBMS). The paper explores and analyzes the autonomic components of Oracle by considering autonomic characteristics. This analysis illustrates how different components of Oracle manage itself autonomically. The research is focused to find and earmark those areas in Oracle where the human intervention is required. We have performed the same type of research over Microsoft SQL Server and DB2 [1, 2]. A comparison of autonomic components of Oracle with SQL Server is provided to show their autonomic status.

Performance prediction and adaptation for database management system workload using Case-Based Reasoning approach

Abstract Workload management in a Database Management System (DBMS) has become difficult and challenging because of workload complexity and heterogeneity. During and after execution of the workload, it is hard to control and handle the workload. Before executing the workload, predicting its performance can help us in workload management. By knowing the type of workload in advance, we can predict its performance in an adaptive way that will enable us to monitor and control the workload, which ultimately leads to performance tuning of the DBMS. This study proposes a predictive and adaptive framework named as the Autonomic Workload Performance Prediction (AWPP) framework. The proposed AWPP framework predicts and adapts the DBMS workload performance on the basis of information available in advance before executing the workload. The Case-Based Reasoning (CBR) approach is used to solve the workload management problem. The proposed CBR approach is compared with other machine learning techniques. To validate the AWPP framework, a number of benchmark workloads of the Decision Support System (DSS) and the Online Transaction Processing (OLTP) are executed on the MySQL DBMS. For preparation of training and testing data, we executed more than 1000 TPC-H and TPC-C like workloads on a standard data set. The results show that our proposed AWPP framework through CBR modeling performs better in predicting and adapting the DBMS workload. DBMSs algorithms can be optimized for this prediction and workload can be controlled and managed in a better way. In the end, the results are validated by performing post-hoc tests.

A Comparison of Collaborative Access Control Models

Collaborative environments need access control to data and resources to increase working cooperation efficiently yet effectively. Several approaches are proposed and multiple access control models are recommended in this domain. In this paper, four Role-Based Access Control (RBAC) based collaborative models are selected for analysis and comparison. The standard RBAC model, Team-based Access Control (TMAC) model, Privacy-aware Role-Based Access Control (P-RBAC) model and Dynamic Sharing and Privacy-aware RBAC (DySP-RBAC) model are used for experiments. A prototype is developed for each of these models and pros and cons of these models are discussed. Performance and sharing parameters are used to compare these collaborative models. The standard RBAC model is found better by having a quick response time for queries as compared to other RBAC models. The DySP-RBAC model outperforms other models by providing enhanced sharing capabilities.

Personal vs. know-how contacts: which matter more in wiki elections?

The use of online social media is also connected with the real world. A very common example of this is the effect of social media coverage on the chances of success of elections. Previous literature has identified that the outcome of elections can often be predicted based on online public discussions. These discussions can be across various online social network with a special focus on the candidates own accounts. Among many other forms of social media, Wikipedia is a very widely-used self-organizing information resource. The management and administration of Wikipedia is performed using special users which are elected by means of online public elections. In other words, the results of these elections pose as an emergent outcome of a large-scale self-organized opinion formation process. However, due to dynamical, and non-linear interactions besides the presence of mutual dependencies between election participants, a statistical analysis of this data can both be cumbersome as well as inefficient in terms of information extraction. We believe that social network analysis is a more appropriate alternative. It allows for the identification of local and global patterns, identification of influential nodes as well as the contacts involved in the influence. In general, this particular analytic technique can help in examining the internal complex network dynamics. In the current paper, we investigates whether personal contacts matter more than know-how contacts in wiki election nominations and voting participation. We employ the use of standard social network analysis tools such as Pajek and Gephi. The presented work demonstrates the significance of personal contacts over know-how contacts of a person in online elections. We have discovered that personal contacts, i.e. immediate neighbors (based on degree centrality) and neighborhood (k-neighbors) of a person have a positive effect on a person’s nomination as an administrator and also contribute to the active participation of voters in voting. Moreover, know-how contacts, analyzed by means of measures such as betweenness and closeness centralities, have a relatively insignificant effect on the selection of a person. However, know-how contacts, measured in terms of betweenness centrality can positively contribute only to the voting process—primarily due to the role played in passing information around the network. These contacts, also measured in terms of influence domain and PageRank, can play a vital role in the selection of an admin. Additionally, such contacts have a positive association with the voting process in terms of reachability and brokerage roles.

Autonomic workload performance tuning in large-scale data repositories

AbstractThe workload in large-scale data repositories involves concurrent users and contains homogenous and heterogeneous data. The large volume of data, dynamic behavior and versatility of large-scale data repositories is not easy to be managed by humans. This requires computational power for managing the load of current servers. Autonomic technology can support predicting the workload type; decision support system or online transaction processing can help servers to autonomously adapt to the workloads. The intelligent system could be designed by knowing the type of workload in advance and predict the performance of workload that could autonomically adapt the changing behavior of workload. Workload management involves effectively monitoring and controlling the workflow of queries in large-scale data repositories. This work presents a taxonomy through systematic analysis of workload management in large-scale data repositories with respect to autonomic computing (AC) including database management systems and data warehouses. The state-of-the-art practices in large-scale data repositories are reviewed with respect to AC for characterization, performance prediction and adaptation of workload. Current issues are highlighted at the end with future directions.