Data Mining And KDD Technical Research

Running head: DATA MINING AND KDD TECHNICAL RESEARCH 1

Data Mining and KDD Technical Research

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DATA MINING AND KDD TECHNICAL RESEARCH 2

Data Mining and KDD Technical Research

The ways of discovering knowledge and the retrieval of information may be taken as

simple particularly when seen from a terminological perspective. Knowledge Discovery in

Databases(KDD) deals with the discovery of patterns in data to present it in understandable,

useful, novels and valid ways. Information retrieval can further be taken as the retrieval of

information associated with searching the different documents and information present in large

databases. Knowledge management (KM) is another important term when dealing with KDD,

and it is related to knowledge searching carried out by organizations in gaining more

information. Knowledge management systems (KMS) come into play when dealing with the

issue of obtaining and using information.

Various variations exist about data elements particularly concerning their usefulness or

potential validity. Such data elements and their integration change toby tasks, organizations, and

individuals (Kargupta & Joshi, 2001). Relevance is specifically associated with certain types of

data and varies amongst different parties even as information retrieval takes place to meet the

information needs to perform a certain function. Ensuring that retrieved data is comprehendible

is quite tasking and difficult. The contexts matter when dealing with data retrieval

comprehension since different parties understand the data differently. Both KDD and IR are

quite complex issues that have many factors, which affect them. The various factors include

methods and tools utilized in retrieval and search of information, data, and needs seeking traits of

users in the system together with the database size and the form of data therein. Such factors

need to be considered when looking at KDD and IR.

When dealing with IR and KDD, it is important to consider their connection with

database management systems (DBMS). There are four categories of DBMS, which include

DATA MINING AND KDD TECHNICAL RESEARCH 3

complex data with the query and without query simple data with the query and without. Simple

data that does not have a query can only be in paper format. The other data types are shown by

object-relational, object-oriented and relational DBMS (Kargupta & Joshi, 2001). Knowledge

discovery is another aspect that is affected by how information is queried from the DBMS

together with the information retrieved. All knowledge management systems have an RDBMS

in them, which is mostly improved version of databases particularly in IR and knowledge

discovery (KD) which are its Functionalities.which is the OODBMS. The database systems are

associated with new issues about the KD and IR.

The various database management systems are greatly associated with IR and KD which

are functionalities affected in their process of coming up with new DBMS. The filing structure

that brings problems in IR and KD in the databases is preset filing. The system lacks an

automated search, which was present in the old DBMS. Relational Database Management has

helped in dealing with the problems associated with new DBMS particularly in the functionality

associated with IR and KD. There is further the need to consider automating various features

associated with KD and IR, which include data evaluation, interpretation, transformation, pre-

processing and selection.

IR and KD impose various limitations on the pre-filing structure. The

informational professionals are expected to be aware of the limitations and the way it

influences the traditional filing system. Technically, the database such as DBMS is not an

automated searching system, but it is only made by searching through using categories of

pre-designed and file descriptions, for example, catalogs and library, or browsing. KD

and IR only present the difficulties of simple filing structures which were replicated by

DATA MINING AND KDD TECHNICAL RESEARCH 4

the file structures that are computer supported (Kargupta & Joshi, 2001). However, these

problems were solved through the introduction of RDM (Relational Database Model).

After Relational Database Model was introduced, RDMS was adopted widely by

Information Companies. It was meant to control the wide range of social and commercial

ventures and the increasing growth of a compelling collection of data and storage technologies.

Through the use of RDBMS, flexibility has been provided in the organizations dealing with data

retrieval in comparison to the traditional data filing systems. The IR methods used in the

conventional system were fixed depending on the needs of the users or description of the

parameters used in searching. More organizations have adopted this method, especially the ones

who possess larger and developing databases, depending on the essential tools for IR and KD.

Data warehousing and data mining are the research focus in most companies to help respond to

the problem associated with information management issue.

Fayyad et al. define data warehousing as the collection and cleaning of transactional data

thus availing it for analysis on the internet and supporting decisions to be made. It focuses on

data collection methodically and pre-processing of information for particular analytical purposes.

The data is usually integrated, oriented to subject and stamped to time thus allowing interactive

analysis in the process of decision making (Holmes, 2012). There are various sources in which

data warehousing can be integrated, and it helps to enrich the data and create a broader context of

information value.

Data mining can be described as the use of particular algorithms to a set of data for the

purpose of extracting patterns of data. The process focuses on improvement of the applications

of sets of extensive data and the response of IR response. The algorithms that have been applied

in data mining and received a large share due to the attention provided in the Decision Support

DATA MINING AND KDD TECHNICAL RESEARCH 5

System (DSS) development and research in RDMS, since the results are applicable

immediately in the companies dealing with decision making, for example, sales, medical,

financial, and insurance services.

Inspirations and Intentions for the Technology

IR has an ultimate goal of producing and recommending relevant information to

the applicants, as described as Rocha. The same motivation can be applied to the

development of the KDD systems and general methods, especially when the DBMS is

being refined (Etzioni, 2014). The research concerning the development of IR and KD

functionality relates to collecting storing and retrieval of information. There are topics

which have been given more attention such as detection of change, summarization, data

translation, timelessness, and aggregation.

The automation of both IR and KD has also been given more attention to the data

collection methods and storage used and even foundations of statistical on the retrieval

and search foundations. Though complication is experienced, manual analysis of all the

records which exists and vast fields to be covered is impractical. The data handling

methods that are automated will have more demand since it provides more flexible

results.

DATA MINING AND KDD TECHNICAL RESEARCH 6

References

Etzioni, O. (2014). The battle for the future of data mining. Proceedings of the 20th ACM

SIGKDD international conference on Knowledge discovery and data mining - KDD

'14. doi:10.1145/2623330.2630816

Extending Metalearning to Data Mining and KDD. (n.d.). Metalearning, 73-90.

doi:10.1007/978-3-540-73263-1_5

Holmes, G. (2012). Developing data mining applications. Proceedings of the 18th ACM

SIGKDD international conference on Knowledge discovery and data mining - KDD

'12. doi:10.1145/2339530.2339569

Kargupta, H., & Joshi, A. (2001). Data mining "to go". Tutorial notes of the seventh ACM

SIGKDD international conference on Knowledge discovery and data mining - KDD

'01. doi:10.1145/502787.502791

Kargupta, H., & Joshi, A. (2001). Data mining "to go". Tutorial notes of the seventh ACM

SIGKDD international conference on Knowledge discovery and data mining - KDD

'01. doi:10.1145/502786.502791

Zaiane, O. R., Chen, J., & Goebel, R. (2007). DBconnect. Proceedings of the 9th WebKDD

and 1st SNA-KDD 2007 workshop on Web mining and social network analysis -

WebKDD/SNA-KDD '07. doi:10.1145/1348549.1348558

DATA MINING AND KDD TECHNICAL RESEARCH 7

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