scholarly journals Evaluation of Replication Mechanisms on Selected Database Systems

2020 ◽  
Vol 9 (4) ◽  
pp. 249
Author(s):  
Tomáš Pohanka ◽  
Vilém Pechanec
Keyword(s):  
Spatial Data ◽  
Database Systems ◽  
Replication Process ◽  
Activity Data ◽  
Database Replication ◽  
Network Bandwidth ◽  
Asynchronous Replication ◽  
Ecological Unit ◽  
Microsoft Windows ◽  
Database Servers

This paper is focused on comparing database replication over spatial data in PostgreSQL and MySQL. Database replication means solving various problems with overloading a single database server with writing and reading queries. There are many replication mechanisms that are able to handle data differently. Criteria for objective comparisons were set for testing and determining the bottleneck of the replication process. The tests were done over the real national vector spatial datasets, namely, ArcCR500, Data200, Natural Earth and Estimated Pedologic-Ecological Unit. HWMonitor Pro was used to monitor the PostgreSQL database, network and system load. Monyog was used to monitor the MySQL activity (data and SQL queries) in real-time. Both database servers were run on computers with the Microsoft Windows operating system. The results from the provided tests of both replication mechanisms led to a better understanding of these mechanisms and allowed informed decisions for future deployment. Graphs and tables include the statistical data and describe the replication mechanisms in specific situations. PostgreSQL with the Slony extension with asynchronous replication synchronized a batch of changes with a high transfer speed and high server load. MySQL with synchronous replication synchronized every change record with low impact on server performance and network bandwidth.

Object-Relational Spatial Indexing

2011 ◽  
pp. 49-80
Author(s):  
Hans-Peter Kriegel ◽  
Martin Pfeifle ◽  
Marco Potke ◽  
Thomas Seidl ◽  
Jost Enderle
Keyword(s):  
Spatial Data ◽  
Concurrency Control ◽  
Buffer Management ◽  
Database Systems ◽  
Data Types ◽  
Spatial Access ◽  
Access Methods ◽  
Object Relational ◽  
Relational Database Systems ◽  
Spatial Access Methods

In order to generate efficient execution plans for queries comprising spatial data types and predicates, the database system has to be equipped with appropriate index structures, query processing methods and optimization rules. Although available extensible indexing frameworks provide a gateway for seamless integration of spatial access methods into the standard process of query optimization and execution, they do not facilitate the actual implementation of the spatial access method. An internal enhancement of the database kernel is usually not an option for database developers. The embedding of a custom, block-oriented index structure into concurrency control, recovery services and buffer management would cause extensive implementation efforts and maintenance cost, at the risk of weakening the reliability of the entire system. The server stability can be preserved by delegating index operations to an external process, but this approach induces severe performance bottlenecks due to context switches and inter-process communication. Therefore, we present the paradigm of object-relational spatial access methods that perfectly fits to the common relational data model, and is highly compatible with the extensible indexing frameworks of existing object-relational database systems, allowing the user to define application-specific access methods.

MULTI-KEY INDEX FOR DISTRIBUTED DATABASE SYSTEM

2005 ◽  
Vol 15 (02) ◽  
pp. 433-438
Author(s):  
MD. SHAZZAD HOSAIN ◽  
MUHAMMAD ABDUL HAKIM NEWTON
Keyword(s):  
Distributed Databases ◽  
Database Systems ◽  
Distributed Database ◽  
Index Model ◽  
Network Bandwidth ◽  
Distributed Database Systems ◽  
Efficient Access ◽  
Distributed Database System ◽  
Bit Vector ◽  
Centralized Database

In this paper we present a multi-key index model that enables us to search a record with more than one attribute values in distributed database systems. Indices provide fast and efficient access of data and so become a major aspect in centralized database systems. Most of the centralized database systems use B + tree or other types of index structures such as bit vector, graph structure, grid file etc. But in distributed database systems no index model is found in the literature. Therefore efficient access is a major problem in distributed databases. Our proposed index model avoids the query-flooding problem of existing system and thus optimizes network bandwidth.

Big Data Computation Model for Landslide Risk Analysis Using Remote Sensing Data

2018 ◽  
pp. 22-33 ◽  
Author(s):  
Venkatesan M. ◽  
Prabhavathy P.
Keyword(s):  
Big Data ◽  
Spatial Data ◽  
Remote Sensing Data ◽  
Database Systems ◽  
Landslide Risk ◽  
Biomedical Sciences ◽  
Useful Knowledge ◽  
Data Mining Algorithms ◽  
Classifier Performance ◽  
Mining Algorithms

Effective and efficient strategies to acquire, manage, and analyze data leads to better decision making and competitive advantage. The development of cloud computing and the big data era brings up challenges to traditional data mining algorithms. The processing capacity, architecture, and algorithms of traditional database systems are not coping with big data analysis. Big data are now rapidly growing in all science and engineering domains, including biological, biomedical sciences, and disaster management. The characteristics of complexity formulate an extreme challenge for discovering useful knowledge from the big data. Spatial data is complex big data. The aim of this chapter is to propose a multi-ranking decision tree big data approach to handle complex spatial landslide data. The proposed classifier performance is validated with massive real-time dataset. The results indicate that the classifier exhibits both time efficiency and scalability.

scholarly journals Mobile Agents Approach for Transaction Processing in Mobile Database Systems – Mobile Learning Environment

2011 ◽  
Vol 6 (1) ◽  
pp. 74-83
Author(s):  
Sheng-Hung Chung ◽  
Ean-Teng Khor
Keyword(s):  
Learning Environment ◽  
Mobile Learning ◽  
Mobile Agent ◽  
Mobile Agents ◽  
Database Systems ◽  
Wireless Transmission ◽  
Handheld Devices ◽  
Replication Process ◽  
Mobile Learning Environment ◽  
Central Database

This paper describes the architecture of using the Mobile Agent and ACID (Atomicity, Consistency, Isolation and Durability) in Mobile e-Learning (mLearning). In the coming years, there will be an incremental amount of mobile learning experiments for the purpose of implementing mobile ICTs into mainstream education. In this article, the Replication Agent and Snapshot Agent architecture is proposed as an effective way to overcome the problem of heavy loading on the limited bandwidth used in wireless transmission for mobile learning environment merge replication process. The implementation of mobile agents as middleware for mLearning environment is to provide database replication between learners and off-site database e.g., Knowledge Management Centre's (KMC) databases using handheld devices. The approach using the combination of both these agents plays an important role in wireless transmission. It provides an intelligent solution to the limitation of the wireless bandwidth by lowering the bandwidth taken up during the bursts of mobile transactions. In this proposed technique, a mLearning database is determined as a Snapshot publisher where a compact edition of files and information is generated at the snapshot location which resides on the central database server. Learners may retrieve quick bursts of information through mobile applications wirelessly. The Replication Agent offers the flexibility to move from one site (user) to another (central database) for essential data synchronization. In order to provide learners with appropriate courses, a prototype on mLearning platform supporting three-layered structure and device adapting was put forward. The architecture of the platform discussed in this study as the mobile agent approach would facilitate more widespread use of mLearning, including in courses discussions between learners and academicians.

scholarly journals Soft Computing Techniques in Spatial Databases

2010 ◽  
pp. 49-71
Author(s):  
Markus Schneider
Keyword(s):  
Soft Computing ◽  
Spatial Data ◽  
Type System ◽  
Type Systems ◽  
Database Systems ◽  
Data Types ◽  
Spatial Objects ◽  
Soft Computing Techniques ◽  
Definition Of ◽  
Spatial Vagueness

Spatial database systems and geographical information systems are currently only able to support geographical applications that deal with only crisp spatial objects, that is, objects whose extent, shape, and boundary are precisely determined. Examples are land parcels, school districts, and state territories. However, many new, emerging applications are interested in modeling and processing geographic data that are inherently characterized by spatial vagueness or spatial indeterminacy. Examples are air polluted areas, temperature zones, and lakes. These applications require novel concepts due to the lack of adequate approaches and systems. In this chapter, the authors show how soft computing techniques can provide a solution to this problem. They give an overview of two type systems or algebras that can be integrated into database systems and utilized for the modeling and handling of spatial vagueness. The first type system, called Vague Spatial Algebra (VASA), is based on well known, general, and exact models of crisp spatial data types and introduces vague points, vague lines, and vague regions. This enables an exact definition of the vague spatial data model since we can build it upon an already existing theory of spatial data types. The second type system, called Fuzzy Spatial Algebra (FUSA), leverages fuzzy set theory and fuzzy topology and introduces novel fuzzy spatial data types for fuzzy points, fuzzy lines, and fuzzy regions. This enables an even more fine-grained modeling of spatial objects that do not have sharp boundaries and interiors or whose boundaries and interiors cannot be precisely determined. This chapter provides a formal definition of the structure and semantics of both type systems. Further, the authors introduce spatial set operations for both algebras and obtain vague and fuzzy versions of geometric intersection, union, and difference. Finally, they describe how these data types can be embedded into extensible databases and show some example queries.

Incremental Data Allocation and Reallocation in Distributed Database Systems

2011 ◽  
pp. 137-160 ◽  
Author(s):  
Amita Goyal Chin
Keyword(s):  
Database Systems ◽  
Distributed Database ◽  
Database System ◽  
Data Allocation ◽  
Distributed Database Systems ◽  
Data Reorganization ◽  
Incremental Growth ◽  
Efficient Data ◽  
Distributed Database System ◽  
Database Servers

In a distributed database system, an increase in workload typically necessitates the installation of additional database servers followed by the implementation of expensive data reorganization strategies. We present the Partial REALLOCATE and Full REALLOCATE heuristics for efficient data reallocation. Complexity is controlled and cost minimized by allowing only incremental introduction of servers into the distributed database system. Using first simple examples and then, a simulator, our framework for incremental growth and data reallocation in distributed database systems is shown to produce near optimal solutions when compared with exhaustive methods.

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