Data stream mining is a stimulating field of study that has raised challenges and research issues to be addressed by the database and data mining communities. The following is a discussion of both addressed and open research issues .
Handling the continuous flow of data streams
This is a data management issue. Traditional database management systems are not capable of dealing with such continuous high data rate. Novel indexing, storage and querying techniques are required to handle this non stopping fluctuated flow of information streams.
Minimizing energy consumption of the mobile device
Large amounts of data streams are generated in resource-constrained environments. Sensor networks represent a typical example. These devices …show more content…
If the number of clusters generated for example is changed, it might represent some changes in the dynamics of the arriving stream. Dynamics of data streams using changes in the knowledge structures generated would benefit many temporal-based analysis applications.
Developing algorithms for mining results’ changes
This is related to the previous issue. Traditional data mining algorithms do not produce any results that show the change of the results over time.
Visualization of data mining results on small screens of mobile devices
Visualization of traditional data mining results on a desktop is still a research issue. Visualization in small screens of a PDA for example is a real challenge. Imagine a businessman and data are being streamed and analyzed on his PDA. Such results should be efficiently visualized in a way that enables him to take a quick decision.
Interactive mining environment to satisfy user requirements
Mining data streams is a highly application oriented field. The user requirements are considered a vital research problem to be addressed.
The integration between data stream management systems [4, 40] and the ubiquitous data stream mining approaches
It is an essential issue that should be addressed to realize a fully functioning ubiquitous mining. The integration among storage, querying, mining and reasoning over streaming information would realize robust streaming systems that could be used
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Data mining is another concept closely associated with large databases such as clinical data repositories and data warehouses. However data mining like several other IT concepts means different things to different people. Health care application vendors may use the term data mining when referring to the user interface of the data warehouse or data repository. They may refer to the ability to drill down into data as data mining for example. However more precisely used data mining refers to a sophisticated analysis tool that automatically dis covers patterns among data in a data store. Data mining is an advanced form of decision support. Unlike passive query tools the data mining analysis tool does not require the user to pose individual specific questions to the database. Instead this tool is programmed to look for and extract patterns, trends and rules. True data mining is currently used in the business community for market ing and predictive analysis (Stair & Reynolds, 2012). This analytical data mining is however not currently widespread in the health care community.
As stated above, data mining is often used to solve business decision problems, “it provides ways to quantitatively measure what business users should already know qualitatively” (Linoff, 2004). A growing number of industries are using data mining to become more competitive in their market by primarily focusing on the customers; increasing their customer relationships and increasing customer acquisition.
Data mining is a class of database applications that looks for hidden patterns in a group of data that can be
Today with the ever growing use of computers in the world, information is constantly moving from one place to another. What is this information, who is it about, and who is using it will be discussed in the following paper. The collecting, interpreting, and determination of use of this information has come to be known as data mining. This term known as data mining has been around only for a short time but the actual collection of data has been happening for centuries. The following paragraph will give a brief description of this history of data collection.
Data, Data everywhere. It is a precious thing that will last longer than the systems. In this challenging world, there is a high demand to work efficiently without risk of losing any tiny information which might be very important in future. Hence there is need to create large volumes of data which needs to be stored and explored for future analysis. I am always fascinated to know how this large amount of data is handled, stored in databases and manipulated to extract useful information. A raw data is like an unpolished diamond, its value is known only after it is polished. Similarly, the value of data is understood only after a proper meaning is brought out of it, this is known as Data Mining.
This master thesis addresses the data mining area known as closed itemset mining. The work program includes analysis a one of well-known algorithms from the literature, and then modifying these algorithm in order to optimize their performance by reduce the number of frequent pattern.
We are seeing today widespread and explosive use of database technology to manage large volumes of business data. The use of database systems in supporting applications that employ query based report generation continues to be the main traditional use of this technology. However, the size and volume of data being managed raises new and interesting issues. Can we utilize methods wherein the data can help businesses achieve competitive advantage, can the data be used to model underlying business processes, and can we gain insights from the data to help improve business processes? These are the goals of Business Intelligence (BI) systems, and Data Mining is the set of embeddable (in BI systems) analytic methods
Data mining functions differently from a classic database interrogation in which, the database inquiries ask for the retrieval of stored information. Datamining is performed in static data collections known as data warehouses. Whereas the online operational databases undergo upgradations. Finding patterns in dynamic system involves much complexity rather than in a static system. The usage of datamining is not only limited to the computer field but also emerged into various disciplines.
This research paper is about the Comparative analysis of three data mining software’s selected based on four important criteria Performance, Functionality, Usability and Ancillary Tasks support. “Data Mining is a field of study that is gaining importance and is used to explore data in search of patterns or relationships between variables and is applied to new data used for predictions”. (Statistics – Textbook. (n.d.). Retrieved November 17, 2015). Selection of the appropriate data mining tools is critical to any research or business and this could impact the business in terms of money, resources and time. Data experts
As Big Data problems evolve, each application have its own characteristics with respect to their data and analysis process. Firstly, besides the huge amount of historical data, streaming data plays an important role. For instance, GPS ground stations do monitor and predict geological events on earthquakes generates lots of real time data which needs streaming data processing. Automatic trading systems in stock market needs dynamic
As an obvious fact, we have lots of data in various fields. Actually, it is estimated that the amount of useful data produced will be over 15 zettabytes by 2020, compared with 0.9 zettabytes in 2013. [IDC 's Study 1] This has led to an unavoidable challenge, however, data users have to figure out a way to properly store and effectively analyze the large-scale data.\
Data mining is a new technology which could be used in extracting valuable information from data warehouses and databases of companies and governments. It involves the extraction of hidden information from some raw data. It helps in detecting inconsistency in data and predicting future patterns and attitude in a highly proficient way. Data mining is implemented using various algorithm and framework, and the automated analysis provided by this algorithm and framework go ahead of evaluation in dataset to providing solid evidences that human experts would not have been able to detect due to the fact that they
Abstract - Detecting the changes and reacting on them is an interesting research topic in current era. Concept drift detection is comes under data stream mining. Process which takeout information from data stream which continuously generated called data stream mining. Normally in data set the data is stationary but problem arises when data is continuously generated that is data stream. So in that case the detection of concept drift is an important task. There are various techniques for drift detection. This paper focuses on some main technique of drift detection.