Innovative Database Architecture for Egovernance

Advanced Architecture for Complex Data sets

Egovernance applications generally involve many to many relationships. Each application has a need for not only identifying each entity inside the application uniquely but also embedding the hierarchical relationships within such entities as part of such unique identification codes. Egovernance applications require what may be called as a hierarchically relational data model.

Take for example the inspection and monitoring functions of governments. These involve the collection, collation, and correlation of data elements belonging to different subjects, rules or norms. The same entity may have to be examined from different perspectives. Every observation made during such scrutiny will have to be related to the exact part of a machine or the precise location of an event. Such a unique identification schema becomes important not only for precisely associating data with the concerned entity but also making such data useful for remedial actions and predictive analysis.

Conventional architecture could no doubt be used to deal with such data needs but at great cost and considerable loss in efficiency. The new architecture incorporates two innovations, which help handle even the most complex data sets with ease and economy View illustration.
First, it incorporates architectural facilities for creation of independent data trees for any number of subjects or domains with unlimited hierarchical nodes. This architecture also enables precise identification of the whole and its parts in a hierarchy with the help of automatically generated codes that not only serve as unique identifiers but also describe the parent child relationship of a particular code within the entire hierarchy.

Secondly, this data model also provides for associating, within the scope of any application, multiple data trees with each other. It has in built facilities for relating any precise node on the data tree to any one or more nodes on one or more data trees. This makes the new architecture ideal for handling many to many relationships between complex data sets without compromising normalization needs.

Nested Forms for handling Dynamic Data Trees

An inspection of a college for example, may involve assessing its performance on many fronts such as course content, teaching facilities, adequacy and quality of faculty members, performance of students in different examinations, etc. Although, the new architecture provides facilities for generating independent data trees for all these domains and associating any data point on any data tree with any other data point belonging to any other data tree, there will still be a problem. The inspector will come to know about the exact number of courses, the details of the faculty members, laboratory facilities etc. only while the carrying out the inspection.

Data trees are analogous to the real trees in the way they grow. It is never possible predict when a new branch or leaf of bud would appear. Nor is it feasible to first get a unique ID generated by the administrator for the new branch and then start the data entry process. For example, an existing rule may have been amended just yesterday to include two more clauses.

This new approach uses a unique design to let the user create the entities as and when the need arises following a predefined hierarchical entity structures. All the data entry forms inside the application are nested together in to a knowledge tree depending upon the child parent relationship that each form has with the other. The user has the option climb up to any of the branches or cross over to any other branch or just climb down after or without doing any work. The user could choose any part of the tree to enter data and take reports for only the data entered. Data fed in to any part of the application could also be imported in to any other part or in to any other related application subject to of course security permissions.. See Chart

A very interesting feature of this model is that its nested architecture is not limited to the data inserted in to the relational database. It also extends to the files that users store in to folders at the time of entering data in to the application. This makes the new architecture ideal for any knowledgebase application as well.

The most amazing feature of this architecture is that all these innovations are available inside a tool. This tool can be used to create and replicate applications very quickly. Applications could be designed, tested, and refined without having to bother about attendant costs. It really does not cost much to make changes inside applications created using this tool.

Egovernance for ensuring security

It is unfortunate that in the age of information technology, we still believe that raising armies, increasing the number security cordons, legislating more laws and inventing more secure locks would guarantee security.

What has been the net outcome of all the money that we have spent so far in the name of strengthening intelligence and beefing up security? Are we feeling more secure?

Are we still going to rely on moles to gather intelligence and save our lives? Can we afford to keep on building fortresses that are impregnable? How much more should we need to pay for security?

It is time that governments shed their imperialistic approach to ensuring security. Only well designed egovernance systems can give governments the real power to combat terror and crime.

Knowledge is power and only information technology based intelligence gathering could help contain the growing threat to all forms of security including social and economic security. Timely information about a stranger taking a house on rent or checking in to a hotel is more important for preventing acts of terrorism than the checking of vehicles on the roads that takes place after every terrorist attack.

The real problem area is the ineffective manner in which governments collect and manage information. If only the vast amounts of data that is routinely collected by the multitude of government agencies could be aggregated and correlated, the government could have a veritable knowledgebase for not only forecasting potential trouble spots but also initiating timely preventive action. Information collected and stored in unstructured paper forms and documents and observations written in non-parameterized text hardly lend themselves for any meaningful aggregation and analysis. Added to this is the use of multiplicity of overlapping classifications and entity identification codes that severely limit the extent of correlating such data.

Only good egovernance systems could help translate all the information that is routinely collected by governments in to a reservoir of knowledge. The effectiveness with which such knowledge bases could be used to minimize the lead-time or response time would certainly depend on the quality of databases.

Egovernance databases do require a radically different architecture than what enterprise solutions offer as of now. The key requirement of any egovernance database is the need for dynamically creating Unique Identification Codes for everything under the sun. It also calls for embedding in to these codes entity hierarchies extending up to 10 to 11 levels of child parent relationships.

Another basic requirement of good egovernance applications is the need for standardization and harmonization of the various classification systems that governments use. Equally important would also be to develop universally applicable parameterised values for representing qualitative data.

All these challenges do appear to be daunting until one realizes what has already been accomplished by an application currently being used by the Directorate General of Mines Safety (DGMS), Dhanbad, India. It certainly provides the basic architecture for a good egovernance application which when adopted on a national scale can create the much-needed knowledgebase necessary for building predictive intelligence.