Introduction of Data Mining

             Databases represent piles of structured data sets regarding a particular topic. An organized form of data enables the researchers, economistic and business organization to get their desired information just by searching some key words. In the modern world, databases are quite common in use at workplaces, libraries, and labs having organized data into columns, tables, and rows. Public databases available online at open access enable the researchers to analyze and manage complex data set easily to reach at their desired information. Searching data and information stored in the data sets and databases are referred to as the data mining process. In the present work, the data mining process is analyzed while considering the techniques and tools used in the data mining process. Present work is consist of two examples or scenarios from public databases are known as Shared Socioeconomic Pathways (SSP) database and Global Energy Assessment (GEA) database. In this analysis of techniques to discover logic, scenarios, and logic are identified in both selected databases.

Analyzing Techniques to Discover Logic of Data Mining

Data mining is consist of several theories and techniques to be used for the extraction of knowledge from various large size data volumes and databases.  Piles of data cannot be analyzed effectively without the use of data mining techniques and tools. Some common data mining techniques and tools includes Efficient Handling of Complex and Relational Data, Database Analysis, Programming Tool, Dynamic Data Dashboards, Knime, Popular Tools for Data Mining, Python based Orange and NTLK, WEKA, Text Analysis, Rapid Miner (erstwhile YALE), and Relevance and Scalability of Chosen Data Mining Algorithms (Gulipalli).

 Data mining tools also help flag data that do not meet the logic guideline for human intervention and review. Common examples of logic in data mining are fuzzy logic and forefront logic. Fuzzy data support the data mining process by making it accredit to cope with uncertain situations in the real world (Gulipalli). Data mining tools and software support to flag data having uncertain conditions and no proper logic guideline. Orange, Oracle Data mining, Weka, and Sisense are important data mining tools in this manner.

Input should meet logic in data sets. In case, when input does not meet or follow logic then there come problems in data mining. Data mining require logic in input to have better output and outcomes no matter it relates to which kind of dataset. There are two examples of public data bases presented below along with scenario and logic identification. These examples are related to the public databases known as Global Energy Assessment (GEA) and Shared Socioeconomic Pathways (SSP) database.

·         SSP Database of Data Mining

            Shared Socioeconomic Pathways (SSP) database is a public database that consists of large piles of data relevant to the social and economic factors. Shared Socioeconomic Pathways (SSP) database also provide data mining opportunities to the climate change researchers by integrating analysis of vulnerabilities, mitigation, and future climate effects. In the database, input meets with the logic to make researchers capable to collect their desired information from this database. Without this, it was not possible and so easy for researchers to get quantitative data about climate changes and future impact of these climate changes (Tntcat.iiasa.ac.at). A number of scenarios are included in this public database. IAM scenarios of Shared Socioeconomic Pathways (SSP) database is recently updated and extended. Updates and extension of SSP IAM scenarios include a project for greenhouse gas emission (concerning with scenario MIP), extension in reporting of existing Shared Socioeconomic Pathways (SSP) database scenarios considering new variables, and SSP-based mitigation scenarios for radiative forcing target of 1.9w/m2. Shared Socioeconomic Pathways (SSP) database is also concerned with integrated assessment scenarios (Tntcat.iiasa.ac.at).         

·         Global Energy Assessment (GEA) Database of Data Mining

         Global Energy Assessment (GEA) Database as a public database serves as a central data repository for the dissemination of database scenario related information. In the Global Energy Assessment (GEA) database, scenarios are used for the purpose to enable pathway choosing regarding the data presented in the database (Iiasa.ac.at). Simultaneously, we can choose a number of pathways (multiple pathways) in case “series” is used as the aggregation method in the database. Moreover, three Global Energy Assessment (GEA) pathway groups include Global Energy Assessment (GEA)-Mix, simply labeled Global Energy Assessment (GEA)-Efficiency, and GEA-Supply. The following figure 1 represents the scenario development process in Global Energy Assessment (GEA)  public database.

The following presented image elaborate on the logic scenario used in Global Energy Assessment (GEA)  database. The systematic scenario used in the database ensure the flow of the information.   

Figure 1 GEA Database

In this GEA database, the input is aligned with the logic. In case of input not meeting the logic researchers will become unable to ensure effective and successful data mining.

Conclusion on Data Mining

           The whole discussion concludes that databases organize complex data in the simplified form. Data mining is an important process to collect information from structured datasets and databases. Logic like fuzzy logic and forefront logic as important logic in data mining enables storing an arrangement of complex data in an authentic and logical way. Summarizing the outcomes of database analyzing we can say that Shared Socioeconomic Pathways (SSP) database use several scenarios and development processes aligned with the logic. Input meeting logic accomplishes the prime and major goal of database development. While on the other hand, GEA database use integrated scenario along with input logic to make researchers capable to collect information about improving energy security, danger in climate change and other climate change related data.  

References of Data Mining

Gulipalli, Gopinadh. 12 Data Mining Tools and Techniques. 2015. <https://www.invensis.net/blog/data-processing/12-data-mining-tools-techniques/>.

Iiasa.ac.at. Public GEA Scenario Database. 2019. <http://www.iiasa.ac.at/web-apps/ene/geadb/dsd?Action=htmlpage&page=about>.

Tntcat.iiasa.ac.at. SSP Database (Shared Socioeconomic Pathways) - Version 2.0. 2019. <https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=about>.