Multi-dimensional database technology based on artificial intelligence

Artificial intelligence and database technology are both important fields of computer science. The research results show that more and more industries have a growing demand for artificial intelligence and database. The combination of these two technologies will certainly bring a broader prospect to computer application. This paper mainly discusses the necessity, importance and development of multi-dimensional database technology based on artificial intelligence, introduces the combination strategy of artificial intelligence and multidimensional database and the current research field, and gives the research direction.

A Simpler and Semantic Multidimensional Database Query Language to Facilitate Access to Information in Decision-making

OLAP and multidimensional database technology have contributed significantly to speed up and build confidence in the effectiveness of methodologies based on the use of management indicators in decision-making, industry, production, and services. Although there are a wide variety of tools related to the OLAP approach, many implementations are performed in relational database systems (R-OLAP). So, all interrogation actions are performed through queries that must be reinterpreted in the SQL language. This translation has several consequences because SQL language is based on a mixture of relational algebra and tuple relational calculus, which conceptually responds to the logic of the relational data model, very different from the needs of the multidimensional databases. This paper presents a multidimensional query language that allows expressing multidimensional queries directly over ROLAP databases. The implementation of the multidimensional query language will be done through a middleware that is responsible for mapping the queries, hiding the translation to a layer of software not noticeable to the end-user. Currently, progress has been made in the definition of a language where through a key statement, called aggregate, it is possible to execute the typical multidimensional operators which represent an important part of the most frequent operations in this type of database.

Evalution of Dataminig for Prediction Techniques Based on Weka Tool

Data mining is a famous technological innovation that converts piles of data into beneficial knowledge, which can assist the data proprietors/users make knowledgeable choices and take clever movements for their personal advantage. In unique terms, facts mining looks for hidden patterns amongst widespread sets of data that may assist to apprehend, expect, and guide destiny conduct. A greater technical explanation: information mining is the set of methodologies utilized in reading data from diverse dimensions and perspectives, finding formerly unknown hidden styles, classifying and grouping the information and summarizing the diagnosed relationships. The elements of facts mining consist of extraction, transformation, and loading of data onto the statistics warehouse device, handling information in a multidimensional database gadget, offering get right of entry to to commercial enterprise analysts and it experts, reading the records with the aid of tools, and supplying the data in a useful layout, inclusive of a graph or desk. That is completed with the aid of identifying courting using classes, clusters, associations, and sequential patterns by using the use of statistical analysis, gadget leaning and neural networks

A Novel Method of Rapid Miner for the Data Mining Applications

Information mining is a renowned mechanical development that changes over heaps of information into useful learning, which can help the information owners/clients settle on educated decisions and take shrewd developments for their own bit of leeway. In remarkable terms, actualities digging searches for shrouded designs among boundless arrangements of information that may help to catch, expect, and direct fate lead. A more prominent specialized clarification: data mining is the arrangement of procedures used in perusing information from various measurements and points of view, finding once in the past obscure shrouded styles, ordering and gathering the data and condensing the analyzed connections. The components of realities mining comprise of extraction, change, and stacking of information onto the insights stockroom gadget, dealing with data in a multidimensional database contraption, offering get right of section to business undertaking examiners and it specialists, perusing the records with the guide of apparatuses, and providing the information in a valuable format, comprehensive of a chart or work area. That is finished with the guide of distinguishing seeking utilizing classes, groups, affiliations, and successive examples by utilizing the utilization of measurable examination, device inclining and neural systems.

Regular pattern mining on multidimensional databases

Extracting general patterns from a multidimensional database is a tricky task. Designing an algorithm to seek the frequency or no. of occurring patterns and really first-class transaction dimension of a mining pattern, general patterns from a multidimensional database is the objective of the task. Analysis prior to mining required patterns from database hence, Apriori algorithm is used. After the acquiring patterns, they have been improved to many further patterns. Nevertheless, to mine the required patterns from a multidimensional database we use FP development algorithm. Here, now we have carried out a pop-growth procedure to mine fashionable patterns from multidimensional database established on their repute values. Utilizing this opportunity, we studied about recognizing patterns which give the reputation of every object or movements inside the entire database. Whereas Apriori and FP-growth algorithm is determined by the aid or frequency measure of an object set. As a result, to acquire required patterns utilizing these programs one has to mine FP-growth tree recursively which involves extra time consumption. We have utilized a mining process, which is meant for multidimensional recognized patterns. It overcomes the limitations of present mining ways by implementing lazy pruning method followed by showing downward closure property.