Distributed Database Environment Research Articles

Proposed framework for enhancing integrity technique using distributed query operation

Database growth and Storage problems are main concern for large and small enterprises nowadays, which significantly influences the efficiency of database applications. Database archiving is one of the solutions available for management. Many issues have been identified as a result of the use of archive databases, including the elimination of inactive data from online transaction processing systems (OLTP), performance and integrity management. The aim of this paper is to propose a framework for storing OLTP and archiving data in a distributed database environment as part of an integrated system. Maintaining the integrity between an OLTP database and an archiving database is crucial, but even more critical is the performance of the required query. The main aspect of the proposed framework is that it will not only ensure data integrity for primary and unique keys between OLTP and archive database, but it will also improve query performance by introducing parallel processing and query execution plans to maintain that integrity.

In-Network Processing of Skyline Join Queries in Wireless Sensor Networks Using Synopses of Skyline Attribute Value Ranges.

We investigate the in-network processing of a skyline join query in wireless sensor networks (WSNs). While much research was conducted on processing skyline queries in WSNs, skyline join queries were dealt with only in traditional centralized or distributed database environments. However, such techniques cannot be applied to WSNs. Carrying out join filtering, as well as skyline filtering using them in WSNs, is infeasible due to limited memory in senor nodes and to excessive energy consumption in wireless communications. In this paper, we propose a protocol to process a skyline join query in WSNs energy efficiently with only a small amount of memory in each sensor node. It uses a synopsis of skyline attribute value ranges, which is a very compact data structure. The range synopsis is used both in the search of anchor points for skyline filtering and in 2-way semijoins for join filtering. We describe the structure of a range synopsis and present our protocol. To optimize our protocol, we solve some optimization problems. Through implementation and a set of detailed simulations, we show the effectiveness of our protocol. The range synopsis is confirmed to be compact enough for our protocol to work well with the limited memory and energy in each sensor node. For the correlated and random distributions, our protocol significantly outperforms other possible protocols, confirming the effectiveness of an in-network skyline as well as the join filtering capabilities of our protocol.

A two-part multi-algorithm concurrency control optimization strategy for distributed database systems

In this paper, we propose a novel holistic approach to address the issues of concurrency control after an exhaustive examination of the problem and the various forms it can transpire. The proposed strategy was formulated depending on different perspectives that are based on exploring a wide range of algorithms, methods, and strategies proposed in practice and theory that attempted to address the problem and its forms, but only partially succeeded in doing so. Here we proposed a two-part holistic strategy to optimize concurrency control in distributed environments that address a wide range of concurrency control anomalies by taking advantage of several concurrency control algorithms' strengths while minimizing their weaknesses. The novelty of our approach transpires from two interconnected parts that can be applied regardless of the type of distributed database environment. The first is a structured tier-based data classification system based on data sensitivity with respect to serializability requirements and ranges from strict to very relaxed forms of serializability constraints. The second is a concurrency management algorithm that allocates the appropriate concurrency control algorithm to each transaction depending on the type of transaction and/or type of data being accessed from the aforementioned tier-based classification method. Our proposed method also incorporates a priority allocation mechanism within the concurrency management algorithm. Priority is allocated to different tier transactions depending on the tier's level, which in turn reflects data importance and sensitivity. Although our proposed strategy remains an algorithmic approach as we encountered various challenges regarding performance testing of a novel multi-algorithm approach for handling concurrency control in distributed database systems. However, future work involves testing the performance of our proposed strategy either through real-time systems after considerable adjustments or by constructing an appropriate customized simulation framework. Finally, the potentials of the strategy presented here are very promising, hence, we recommend as we are also optimistic that other scholars are encouraged to further exploit the concept of using multiple concurrency control algorithms within the same distributed database environment.

NOVEL PRIVACY PRESERVING ASSOCIATION RULE MINING FOR HORIZONTALLY PARTITIONED DATABASES

Advancement in scientific research depends on the availability and sharing of information and ideas. Many authors proposed many methodologies for isolation preserving data mining for centralized as well as for distributed database. The core intend of this paper work is, to develop efficient methodologies to find privacy preserving association rule mining in centralized as well as in distributed database environment without violating any privacy constraints. The issue of privacy constraints for centralized database environment is entirely different from distributed database environment. So the approaches for privacy preserving association rule mining in centralized are also different from approaches in distributed environment. Achieving privacy preserving goals in both these environments in the process of mining is a challenging task.

Optimization Method for Distributed Database Query Based on an Adaptive Double Entropy Genetic Algorithm

In a distributed database environment, multi-join query optimization is one of the key factors affecting database performance. Genetic algorithms have a good application in dealing with this type of problem. However, the traditional genetic algorithm has the problems of low efficiency and easily falls into the precocity when dealing with query optimization, which is mainly caused by the lack of population diversity. Therefore, this paper sets up a mathematical model for distributed database query optimization and proposes an adaptive genetic algorithm based on double entropy. We introduced a genetic algorithm with two types of entropy: genotype and phenotype. Genotype entropy was used to optimize the distribution of the initial population, ensuring that the initial population has good population diversity. Phenotype entropy is used to optimize the genetic strategy, which can be divided into individual entropy and population entropy. Individual entropy is used to optimize the selection strategy, and population entropy is used to optimize the crossover and mutation operators to maintain the population diversity in the iteration process and accelerate the speed of iteration. The experimental results show that the algorithm proposed in this paper is effective for query optimization of a distributed database.

Using Unlabeled Data Set for Mining Knowledge from DDB

In this paper, two algorithms were introduced to describe two algorithms to describe and compare the applying of the proposed technique in the two types of the distributed database system. The First Proposed Algorithm is Homogeneous Distributed Clustering for Classification (HOMDC4C), which aim to learn a classification model from unlabeled datasets distributed homogenously over the network, this is done by building a local clustering model on the datasets distributed over three sites in the network and then build a local classification model based on labeled data that produce from clustering model. In the one computer considered as a control computer, we build a global classification model and then use this model in the future predictive. The Second Proposed Algorithm in Heterogeneous Distributed Clustering for Classification (HETDC4C) aims to build a classification model over unlabeled datasets distributed heterogeneously over sites of the network, the datasets in this algorithm collected in one central computer and then build the clustering model and then classification model. The objective of this work is to use the unlabeled data to introduce a set of labeled data that are useful for build a classification model that can predict any unlabeled instance based on that classification model. This was done by using the Clustering for Classification technique. Then presented this technique in distributed database environment to reduce the execution time and storage space that is required.

BVAGQ-AR for Fragmented Database Replication Management

Large amounts of data have been produced at a rapid rate since the invention of computers. This condition is the key motivation for up-to-date and forthcoming research frontiers. Replication is one of the mechanisms for managing data, since it improves data accessibility and reliability in the distributed database environment. In recent years, the amount of various data grows rapidly with widely available low-cost technology. Although we have been packed with data, we still have lacked of knowledge. Nevertheless, if the impractical data is used in database replication, this will cause waste of data storage and the time taken for a replication process will be delayed. This paper proposes Binary Vote Assignment on Grid Quorum with Association Rule (BVAGQ-AR) algorithm in order to handle fragmented database synchronous replication. BVAGQ-AR algorithm is capable for partitioning the database into disjoint fragments. Fragmentation in distributed database is very useful in terms of usage, reliability and efficiency. Managing fragmented database replication becomes a concern for the administrator because the distributed database is disseminated into split replica partitions. The result from the experiment shows that handling fragmented database synchronous replication through proposed BVAGQ-AR algorithm able to preserve data consistency in distributed environment.

A summary of the research on the foundation and application of blockchain technology

Blockchain is an emerging distributed database technology. It has the characteristics of decentralization, non-tampering, traceability and final consistency. Blockchain can solve data management problems in untrusted environments. Based on the research and analysis of the blockchain system, this article expounds the application of the blockchain system in the distributed database environment. First, this article introduces the concept of the blockchain system and the classification of the blockchain system from multiple aspects. Then it introduces the data storage technology and data encryption technology adopted by the blockchain system in detail. Finally, the application prospects of the blockchain system in today’s society are introduced.

An improved vertical fragmentation, allocation and replication for enhancing e‐learning in distributed database environment

AbstractE‐learning is the indispensable technique to educate huge number of people and students in short period of time with optimized usage of different kind of required resources. It is employed as a crucial teaching approach by almost all kind of educational institutions all around the world. Since e‐learning involves significant amount of resource utilization and cost, it requires some essential methodology to enhance the current system of e‐learning more efficient. The mere publication of the educational content in websites is not enough. It is very clear that, without applying suitable strategic models and concepts and establishing appropriate communication channels between contributors of e‐learning system, the educational goals cannot be achieved as we desired. Distributed database involves greater contribution in the field of cloud based e‐learning process. Basically, data replication is crucial decision of companies as database distribution can be achieved effectively by the method of database replication which generates the same copies of information called replicas. In this article, we analyze the supremacy of synergetic learning and concentrates on data replication's significance in cloud based learning system. Here we propose an excellent mechanism for data replication and enhancing the performance in terms optimized access and update of data by the determination of exact location of data through dynamic programming. The efficiency of proposed mechanism is clearly illustrated by experimental results.

Analysis and evaluation of the riak cluster environment in distributed databases

ABSTRACT Many institutions and companies undergoing technological developments have been producing large amounts of structured and unstructured data. Special databases are required to deal with such data and NoSQL databases have thus emerged. They are widely used in cloud databases and distributed systems. In the era of big data, these databases provide a scalable solution with high availability. In this context, we need new architectures in order to store more and more different kinds of data. To obtain a good structure for large and diverse data, structures must be tested and analyzed in depth with the use of different benchmark tools. In this paper, we test the Riak key-value database to measure its performance in terms of throughput and latency, where huge amounts of data are stored and retrieved in different sizes in a distributed database environment. The throughput and latency of the NoSQL database in different types of experiments and with different sizes of data are compared. As we increase the data size in the experiments, an increase in the number of threads leads to better throughput and latency factor was reduced. High performance results are obtained for only read operations for all experiments. We observed that performance advanced when there was only read operations compared with a mix of read and update operations. Moreover, our findings intensify the understanding of the distributed database and have to help future developers through the experimental results shown in this paper.

FRAGMENTATION TECHNIQUES FOR IDEAL PERFORMANCE IN DISTRIBUTED DATABASE – A SURVEY

Distributed processing is an operative way to improve the performance of the distributeddatabase systems and thus showing the effectiveness of computational intelligence. Aspecific arrangement of distributed processing through distributed database, where alogically integrated database is physically scattered over several distinct but linkedthrough the computing facilities. Hence, proper fragmentation of data across numeroussites of the network is considered as a significant exploration area in the distributeddatabase environment. Data can be allocated in distinct servers by fragmenting thecomplete or partially database into several pieces with different techniques. Each piece isstored at the different sites. In this paper, an exploration of fragmentation techniques indistributed database is done. Fragmentation techniques had been proved by manyresearches in improving the performance of the distributed database system, increase thedata accessibility, reduce the transfer cost and access time. This paper presents a surveyof numerous fragmentation techniques on distributed database systems.

Cost Saving Replication Technique in Data Grid

Replication is a useful technique for distributed database systems. An existing popular technique that has been used before are Read One-write-All Monitoring Synchronization Transactions System (ROWA-MSTS) and Hierarchical Replication Scheme (HRS). Nevertheless, these techniques have their flaws in terms of replication time taken. Subsequently, ROWA and HRS take more executing time in order to complete a transaction. It is because they have to copy their data to all sites. This research, Binary Voting Fragmented Database Replication (BVFDR) model, the some-data-to-some-sites technique is proposed. In order to handle transactions in the systems, BVFDR considers the neighbouring servers binary vote assignment to its logical grid structure on fragmented data copies. Therefore, it reduces storage capacity. An experiment has been carried out in three replicated servers. The results have been compared with existing techniques such ROWA and HRS. The result shows that BVFDR is able to preserve data consistency and outperformed in terms of time taken for a complete transaction compare to existing techniques. Overall, BVFDR able to handle fragmented data replication as well as capable to manage transaction management in distributed database environment by maintaining consistency of the data through the synchronization approach.

Design and Implementation of RFID Authentication Protocol based on Simple Symmetric Key (SSK) algorithm

RFID uses radio frequency waves to transfer data using components such as tag, the reader and back-end server for device identification. Radio Frequency Identification technology (RFID) providing unique identification and tracking any device that has a tag attached to safeguard the products and protect from unauthorized users. There are many kinds of protocols to resolve these problems have been researched. Though, the security, cost, time and task of RFID are accountable to identify the feasible authentication protocol according to the business environment. In this research article, we proposed new authentication protocol as a method to protect privacy, especially for affordable cost and functionalities and have limited power consumption, memory and effective security process. The proposed protocol called as simple symmetric key (SSK) algorithm using random integers based on modulo 37. The proposed authentication protocol is secure against spoofing and replay attack and also it is suitable to support distributed database environment.

Evolutionary Algorithms for Query Op-timization in Distributed Database Sys-tems: A review

Evolutionary Algorithms are bio-inspired optimization problem-solving approaches that exploit principles of biological evolution. , such as natural selection and genetic inheritance. This review paper provides the application of evolutionary and swarms intelligence based query optimization strategies in Distributed Database Systems. The query optimization in a distributed environment is challenging task and hard problem. However, Evolutionary approaches are promising for the optimization problems. The problem of query optimization in a distributed database environment is one of the complex problems. There are several techniques which exist and are being used for query optimization in a distributed database. The intention of this research is to focus on how bio-inspired computational algorithms are used in a distributed database environment for query optimization. This paper provides working of bio-inspired computational algorithms in distributed database query optimization which includes genetic algorithms, ant colony algorithm, particle swarm optimization and Memetic Algorithms.

Spatial data analysis using association rule mining in distributed environments: a privacy prospect

In this paper, we investigate the privacy issue that is remained in the spatial association rule mining (SARM). The main aim of SARM is to calculate the relationship among attributes according to their geographic locations. However, the major problem of the distributed data mining is the privacy and security issues, which require executing global results without disclosing private information by third parties. The problem of privacy preservation for SARM in distributed environments is controlled by the proposed algorithm, which is able to extract association among different numbers of attributes in geo-graphical distributed database with high privacy. The proposed algorithm is validated in term of data utility rate, efficiency and privacy preservation against the existing algorithms. It has been revealed that this algorithm decreases the execution time, memory requirements, and privacy failure rate when the size of database increases within the geographically distributed database environment.

An illustration to secured way of data mining using privacy preserving data mining

The aim of data mining is to extract useful information from huge source of multiple data. But during the process of data mining, intentionally or unintentionally the data becomes visible and thus vulnerable while handling. Privacy Preserving is a new concept in the area of data mining taking the security issues of users’ data being mined as prime concern. It ensures that privacy of sensitive data will be preserved even after mining by multiple parties. There are various existing methods for privacy preserving data mining which are based on data distortion, clustering, intersection, data distribution etc. The paper discusses few of such privacy preserving data mining techniques. At the end of the paper, a simple mathematical approach for this, is also discussed which is applicable for a number of sites, sharing data on distributed database environment.

Minimum cost rule enforcement for cooperative database access

In this paper, we consider restricted data sharing between a set of parties that wish to provide some set of online services requiring such data sharing. Each party is assumed to store its data in private relational databases, and is given a set of mutually agreed set of authorization rules that specify access to attributes over individual relations or joins over relations owned by one or more parties. The access restrictions introduce significant additional complexity in rule enforcement and query planning as compared with a traditional distributed database environment. We examine the problem of minimum cost rule enforcement which simultaneously checks for the enforceability of each rule and generation of minimum cost plan of its execution. However, the paper is not focused on specific cost functions, but instead of efficient methods for enforcing rules in the face of access restrictions and inter-party data transfer needs. We propose an efficient heuristic algorithm for this minimal enforcement since the exact problem is NP-hard. In some cases, it is not possible to enforce the rules with the regular parties only. In such cases, we need help of trusted third parties (TPs). If all parties trust a single TP, such a party can enforce all unenforced rules, but it is desirable to use the TP minimally. We also consider the extended case where multiple TPs are required since not every regular party can trust a single TP.

Optimal Materialized View Management in Distributed Environment Using Random Walk Approach

<p>Materialized View selection and maintenance is a critical problem in many applications. In large databases particularly in distributed database, query response time plays an important role as timely access to information and it is the basic requirement of successful business application. The materialization of all views is not possible because of the space constraint and maintenance cost constraint. Materialized views selection is one of the crucial decisions in designing a data warehouse for optimal efficiency. Selecting a suitable set of views that minimizes the total cost associated with the materialized views is the key component in distributed database environment. Several solutions have been proposed in the literature to solve this problem. However, most studies do not encompass search time, storage constrains and maintenance cost. In this research work two algorithms are depicted; first for materialized view selection and maintenance in distributed environment where database is distributed, Second algorithm is for node selection in distributed environment. </p><p><em>Journal of Advanced College of Engineering and Management, Vol.1</em>, 2015, 69-75</p>

A New Approach to Re-Fragment in a Distributed Database Environment

The key idea of this paper is to present a novel method for Re-fragmentation in a Distributed Database System that ensures that the Database System is adaptive to the changes in access frequency patterns. Conventionally, fragmentation is done to reduce network transfer cost and communication costs. But with time, changing access patterns at the sites defies the very purpose of fragmentation. In this paper, we have presented an algorithm to maximize and maintain the efficiency of fragmentation in a Distributed Database System. This algorithm takes into account the change in the access patterns and queries; evaluates and quantifies them and performs re-fragmentation periodically according to the updated access frequency patterns. This helps in sustaining the efficiency of fragmentation irrespective of the changing access patterns. The technique used for studying the efficiency of the new algorithm was to employ the new algorithm on a sample Distributed Database system and compare the same with the existing conventional algorithm. The performance of a Distributed Database system is inversely proportional to its communication cost and execution time of queries1,2. On analysis of the presented algorithm, we see that the time complexity of this algorithm is linear. Also the total Network Transfer Cost is considerably lesser than the conventional fragmentation algorithm for Distributed Database. The algorithm presented in the paper is applicable to all Distributed Database systems, which have changing access frequency patterns

Schema Integration Based Merging and Matching Algorithm for Agricultural HDDBs

In any combination of database, a common agreement of the underlying instances concerned to a schema attribute may exist. A distributed database may therefore contain different schemas of same data attributes or different attributes classified from the same population. In this paper, we propose a common schema integration approach to integrating different autonomous agricultural databases of biorefining field of Miscanthus plants. We adopt a model, which is basically relational to some object-oriented properties. We describe methodologies to integrate heterogeneous distributed databases by using this model and show how attribute conflicts and missing attributes can be resolved. We also describe how a query on integrated global schema is managed in heterogeneous agricultural distributed database environment. We give a formal definition and perform extensive experiments to make this integration process strong enough.