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Database and Web Application Trends and Challenges in Systems Driven BioResearch

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The modern technological systems meant for database administration and web application development are rapidly evolving and turning out to be more dynamic. This evolving characteristic of technology has a significant impact on systems-driven biological research, imposes some unique requirements in terms of design, architecture, computational ability, technical infrastructure, etc.

Newer and higher capacity solutions are being proposed day by day to meet the growing expectations. Also, novel methods are being developed to address the challenges related to data integration. Considering these, the bio researchers need to understand and adapt to various approaches and technologies in database administration and web applications. By getting familiarized with the advantages and disadvantages of the available technology, researchers can better exploit its features to enjoy data integration’s optimum potential.

When it comes to biological research, the critical challenges associated with database handling and web application support systems are discussed below. Emerging data storage, processing, and integration technologies should effectively address these issues to facilitate continuous progress in the systems research.

Critical issues related to database

Availability of data

When it comes to multi-site biological researches, data availability is a crucial factor. This deals with the challenges associated with accessing various pieces of data out of the private and public setups, which, also by large, may be influenced by different institutional policies.

Quantity of data

As we can assume, systems research is always data-intensive as well as iterative. The volume of current data will keep on rising as new models evolve and further information gets unveiled. This will, in turn, result in more and more recent data with variations of the old. This cycle is ever ending, and so the data volume will exponentially increase over time. So, ensuring data quality is crucial to keep an eye on in systems-driven biological research.

Data quality

Same as what we had seen above, data quality denotes a complete set of data properties that describes its ability to satisfy the user requirements and expectations. This plays a vital role in information acquisition in a specific area of interest, new learning, and decision-making. Databases should be capable of instituting a foolproof QA mechanism always to ensure that the data provided to the research community is of top quality.

It may seem very easy to enforce the quality measures in the closed setups of biological research. Still, when it comes to social, collaborative research environments, this becomes a big challenge. Compromised data quality as incomplete fields of missing information will create grave issues in terms of data integration and final outputs. To ensure high data quality, after acquiring data, databases should use strict and foolproof quality measures, which should also include manual curation by the experts.

As RemoteDBA points out, it is imperative to have a standardized mechanism to ensure data submission completeness and consistency. Strategies like using data procurement using Semantic aware forms, on-the-spot field validations for data entry with Web scripts, etc. may also help to minimize the proliferation of incompetent and inaccurate data.

Data access

Systems-driven biological researchers may often work with a very diverse set of data, i.e., from varying biological levels as cellular, molecular, organism, etc. So, highly efficient computational frameworks that will serve as data stores and instant data access with simple querying is needed. These data frameworks must also give quick access to by storing these into a central repository and through a uniform and easy to use interface, which can offer access to heterogeneous databases that are geographically separated.

So, with multiple heterogeneous data sources being present, query and extraction of data should not be a problem. The expectation here is to fetch information over a single question to search through several sources and give the output.

Visualization of data

This is the most critical purpose to be served by a database as the researchers need to get an on-the-go visualization of modeled and raw data. This will help them analyze and interpret the complex data, which are left interconnected. Visualizing critical data as networks and pathways will allow the researchers to communicate and record their findings for better understanding easily. However, the irony here is that the users may be overwhelmed by the visualizing deluge by many of the providers rather than getting benefitted out of it when it comes to systems research.

The other primary considerations are for data representations and standards, version control of databases, interoperability with other data stores and systems, computational intensiveness, development capabilities, and, most importantly, security.

Data Integration technologies and approaches

Systems research in terms of data integration is an interdisciplinary topic, which covers meaningful data interpretation from the high-throughput experiments to building effective multiscale models. A continuous need is also there to integrate existing technologies with emerging technologies.

In a centralized model of data integration, there is a unified schema and a massive centralized repository. This gets framed based on schemas of various individual sources of data. The data that comes to this central repository is gathered, integrated, stored, and made available for search. On the other hand, the distributed model of data integration, including mediation and federation approaches. In a federation, the data is left in an expert, fully functional database to maintain autonomy while still offering integrated access to the distributed data. On the other hand, mediation may not store any data on its own rather than provide a simple virtual view of the data’s integrated sources.

One of the most popular data integration techniques was the centralized model, which provides a unified interface to access heterogeneous sources of data. However, the drawback was that it is not capable of supporting any Semantics searches used to fetch hierarchically structured info. By adopting another popular model of distributed database, you may expect access to heterogeneous data resources. However, this demands high-end technology to automate access to remote data resources and manage the data correctly.

Here, we have discussed the current issues and approaches of databases and web applications in systems-driven biological research. Keeping all these points in mind, it is of paramount importance to choose the most appropriate technology for you based on comprehensive research.

 

Source: Karen Anthony is a Business Tech Analyst. She loves to share her tips with friends. She is passionate about gadgets.

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