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Research paper on database management system for xml

And this is particularly true for scientific data.

XML Database

This happens also for business data, but here they had more time to learn. They implemented data architectures, created data warehouse and used data mining to extract information from their data. So why don't study and implement something similar for scientific data?

The solution can be to setup a Scientific Data Management architecture. Scientists normally limit the meaning of Data Management to the mere physical data storage and access layer.

But the scope of Scientific Data Management is much boarder: Below I listed common problem and opportunities in scientific data access. Then I collected what are considered the parts of a Data Management solution.

Scientific Data Management areas

A list of references and examples of data access and scientific data collections follow. The paper ends with more implementation oriented issues: Most of this paper notes and information have been collected and studied for one specific project.

But really the ideas collected are generally applicable to the kind of scientific projects that uses the CSCS computational and visualization services. I try my best to fix them, but not always succeed. Problems and opportunities Problems that can be found in current scientific projects are for example: Limited file and directory naming schemes.

Some project data repositories are simply big flat directories. Scientists retrieve entire files to ascertain relevance. No access to important metadata in scientists' notebooks and heads.

Un-owned data with dubious content after the end of project or PhD thesis. But the increasing of scientific data collections size brings not only problems, but also a lot of opportunities.

One of the biggest opportunities is the possibility of reuse existing data for new studies. The idea is summarized below: Another virtuous effect can be called "discovery by browsing".

  1. A DCS combines the following professional dissertation proposal editor site for school into a single.
  2. Problems and opportunities Problems that can be found in current scientific projects are for example. Physical data handling This layer maps between the physical to the logical data views.
  3. Full text search on articles.
  4. So why don't study and implement something similar for scientific data?

If the data is well described and the data access method is quite flexible, the user can establish unexpected correlations between data items thus facilitating serendipitous discoveries. Last, but not least, remember that the data is composed not only by bytes, but also by workflow definitions, computation parameters, environment setup and so on.

Research paper on database management system for xml

They also have huge data sets to be managed, but they must comply also with industry regulations and rigidly enforce intellectual property protection. The second point is important for each science field, but not as vital as in industry. In this paper we don't touch those specific problems.

  • One of the biggest opportunities is the possibility of reuse existing data for new studies;
  • A database is Pay for my social studies dissertation results an organized collection research paper on database management system for xml of data.

Here I collected a quick list of the most important ones: Creation of logical collections The primary goal of a Data Management system is to abstract the physical data into logical collections.

The resulting view of the data is a uniform homogeneous library collection. Physical data handling This layer maps between the physical to the logical data views. Here you find items like data replication, backup, caching, etc.

XML Database

Interoperability support Normally the data does not reside in the same place, or various data collection like star catalogues should be put together in the same logical collection. Security support Data access authorization and change verification. This is the basis of trusting your data.

Data ownership Define who is responsible for data quality and meaning. Metadata collection, management and access. Metadata are data about data. Persistence Definition of data lifetime. Deployment of mechanisms to counteract technology obsolescence. Knowledge and information discovery Ability to identify useful relations and information inside the data collection. Data dissemination and publication Mechanism to make aware the interested parties of changes and additions to the collections.

General surveys A number of Scientific Data Management surveys and links to research groups are available online. Here are some links I have found useful: Workshop on Interfaces to Scientific Data Archives March This report contains various examples of scientific data management and access issues and solutions.

Problems and opportunities

The most important points are: SciDAC Scientific Data Management Center Terascale computing and large scientific experiments produce enormous quantities of data that require effective and efficient management. The task of managing scientific data is so overwhelming that scientists spend much of their time managing the data by developing special purpose solutions, rather than using their time effectively for scientific investigation and discovery.

The goal of this center is to establish an Enabling Technology Center that will provide a coordinated framework for the unification, development, deployment, and reuse of scientific data management software. Contains interesting references to metadata harvesting and more general information: November [ PPT ] Introduces the kind of research enabled by cheap data storage.

April [ PPT ] This presentation contains an introduction to metadata and the comparison of old one shot and new reuse research methods. It is a general overview with some recommendations like XML usage. Other workshop materials are available. The various presentation and case study on Data Mining and Visualization are interesting.

A new concept and a discovery tool for Astronomy and Astrophysics Research [ PPT or PDF ] In the context of the European Astrophysical Virtual Observatory this presentation survey the most important problems faced by a big archive of scientific data, the role of the Grid and demos some of the potential benefits and discoveries made possible by a good data management system.

  • It is the largest custom masters essay editing service for university industrial research organization in the world with 12 labs on 6 continents;
  • The second point is important for each science field, but not as vital as in industry;
  • Research the latest tools, technologies and techniques.

Real world examples Scientific data access examples Here are some public examples of access to data collections. Almost all have a web browser as user interface. This choice has various interesting features like: Full text search plus similarity, scores, etc.

Full text search on articles. Flytrap Gene expression database in the brain of the fruit fly Drosophila. Uses thumbnails as a guide to the correct dataset. The results are images or movies. The query is by hierarchy only. Protein Data Bank Protein Data Bank is the single worldwide repository for the processing and distribution of 3-D biological macromolecular structure data.

It offers full text search. Provides various options for result display: The images and structures are created on the fly seems.

  • Data dissemination and publication Mechanism to make aware the interested parties of changes and additions to the collections;
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  • Uses thumbnails as a guide to the correct dataset.