25,625 research outputs found
Some Considerations about Modern Database Machines
Optimizing the two computing resources of any computing system - time and space - has al-ways been one of the priority objectives of any database. A current and effective solution in this respect is the computer database. Optimizing computer applications by means of database machines has been a steady preoccupation of researchers since the late seventies. Several information technologies have revolutionized the present information framework. Out of these, those which have brought a major contribution to the optimization of the databases are: efficient handling of large volumes of data (Data Warehouse, Data Mining, OLAP – On Line Analytical Processing), the improvement of DBMS – Database Management Systems facilities through the integration of the new technologies, the dramatic increase in computing power and the efficient use of it (computer networks, massive parallel computing, Grid Computing and so on). All these information technologies, and others, have favored the resumption of the research on database machines and the obtaining in the last few years of some very good practical results, as far as the optimization of the computing resources is concerned.Database Optimization, Database Machines, Data Warehouse, OLAP – On Line Analytical Processing, OLTP – On Line Transaction Processing, Parallel Processing
Storage Solutions for Big Data Systems: A Qualitative Study and Comparison
Big data systems development is full of challenges in view of the variety of
application areas and domains that this technology promises to serve.
Typically, fundamental design decisions involved in big data systems design
include choosing appropriate storage and computing infrastructures. In this age
of heterogeneous systems that integrate different technologies for optimized
solution to a specific real world problem, big data system are not an exception
to any such rule. As far as the storage aspect of any big data system is
concerned, the primary facet in this regard is a storage infrastructure and
NoSQL seems to be the right technology that fulfills its requirements. However,
every big data application has variable data characteristics and thus, the
corresponding data fits into a different data model. This paper presents
feature and use case analysis and comparison of the four main data models
namely document oriented, key value, graph and wide column. Moreover, a feature
analysis of 80 NoSQL solutions has been provided, elaborating on the criteria
and points that a developer must consider while making a possible choice.
Typically, big data storage needs to communicate with the execution engine and
other processing and visualization technologies to create a comprehensive
solution. This brings forth second facet of big data storage, big data file
formats, into picture. The second half of the research paper compares the
advantages, shortcomings and possible use cases of available big data file
formats for Hadoop, which is the foundation for most big data computing
technologies. Decentralized storage and blockchain are seen as the next
generation of big data storage and its challenges and future prospects have
also been discussed
Optimal Control of Applications for Hybrid Cloud Services
Development of cloud computing enables to move Big Data in the hybrid cloud
services. This requires research of all processing systems and data structures
for provide QoS. Due to the fact that there are many bottlenecks requires
monitoring and control system when performing a query. The models and
optimization criteria for the design of systems in a hybrid cloud
infrastructures are created. In this article suggested approaches and the
results of this build.Comment: 4 pages, Proc. conf. (not published). arXiv admin note: text overlap
with arXiv:1402.146
A spatial data handling system for retrieval of images by unrestricted regions of user interest
The Intelligent Data Management (IDM) project at NASA/Goddard Space Flight Center has prototyped an Intelligent Information Fusion System (IIFS), which automatically ingests metadata from remote sensor observations into a large catalog which is directly queryable by end-users. The greatest challenge in the implementation of this catalog was supporting spatially-driven searches, where the user has a possible complex region of interest and wishes to recover those images that overlap all or simply a part of that region. A spatial data management system is described, which is capable of storing and retrieving records of image data regardless of their source. This system was designed and implemented as part of the IIFS catalog. A new data structure, called a hypercylinder, is central to the design. The hypercylinder is specifically tailored for data distributed over the surface of a sphere, such as satellite observations of the Earth or space. Operations on the hypercylinder are regulated by two expert systems. The first governs the ingest of new metadata records, and maintains the efficiency of the data structure as it grows. The second translates, plans, and executes users' spatial queries, performing incremental optimization as partial query results are returned
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