Over the last so many years, a large amount of data has become available like a large amount of collections of photos, genetic information, and network traffic statistics, modern technologies and cheap storage facilities have made it possible to collect huge datasets. But can we effectively use all this data? The ever increasing sizes of the datasets make it imperative to design new algorithms capable of shifting through this data with extreme efficiency.
Figure 1.1
The challenges include capture, storage, search, sharing, transfer, analysis, and visualization. The trend to larger data sets is due to the additional data derivable from analysis of a single large set of related information. As of 2012, limits on the increasing size of data sets that are feasible to process in an efficient amount of time were on the order of terabytes of data. Scientists are regularly trying to find out limitations due to large data sets in many areas, including meteorology, genomics, complex physics simulations, and biological and environmental research. The limitations also affect Internet search, finance and business informatics. Data sets grow in size in part because they are increasingly being collected by universal information-sensing mobile devices, aerial sensory technologies (remote sensing), software records, cameras, microphones, radio-frequency identification experts, and wireless sensor networks. The world's technological per-capita capacity to store data has roughly doubled every 40 months since the 1970s; as of 2010, every day 2.5 terabytes (2.5×1018) of data is being created. The challenge for large enterprises is to determine who should own big information initiatives that stride the entire organization.
Big data i...
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...r low dimensional version of the problem. This algorithm provides large speedups with only very less loss in accuracy. During recent years, several researchers have shown that indeed in many cases approximation enables reduction of the dependence on dimension from exponential to polynomial. In addition, there are many approximate nearest-neighbor algorithms that are more efficient than the exact ones, even though their query time and/or space usage is still exponential in the dimension.
The approximate nearest neighbor algorithm can be best implemented using data structures like basic knn, Kd trees and locality sensitive hashing.
1.5 Approaches followed
This report includes the three approximate nearest neighbor algorithms, the study, implementation and the results and applications related to these three algorithms:
Basic Knn Algorithm
KD Tree Algorithm
LSH
The input of algorithm is Data points with n features and the number of clusters given by K. Initially K centroids are assigned randomly. The points in the dataset are assigned to a cluster based on Euclidean distance.
Big Data is characterized by four key components, volume, velocity, variety, and value. Furthermore, Big Data can come from an array sources such as Facebook, Twitter, call
According to Lisa Arthur, big data is as powerful as a tsunami, but it’s a deluge that can be controlled. In a positive way it provides business insights and value. Big data is data that exceeds the processing capacity of conventional database systems. It is a collection of data from traditional and digital sources inside and outside a company that represents a source of ongoing discovery and analysis. The data is too big, moves to fast, or doesn’t fit the structures of the database architecture. Daily, we create 2.5 quintillion bytes of data. In the last couple years we have created 90% of data we have in the world. This data comes from many places like climate information, social media sites, pictures or videos, purchase transaction records, cell phone GPS signals, and many more places. From the beginning of recorded time through 2003 users created 5 billion gigabytes of data. 2011, the same amount was created every couple days. 2013, we created that same amount every ten minutes. Some users prefer to constrain big data into digital inputs like web behavior and social network interactions. The data doesn’t exclude traditional data that is from product transaction information, financial records and interaction channels.
Big Data is changing the arena for big businesses. Big Data is the technology trend that has made it possible for businesses to better understand their markets. Big Data is the new natural resource, the new “oil.”
...hniques such as correlation-based matching, minutiae-based matching, and pattern-based (or image-based) matching uses standard dataset for testing purpose. But Practically due to some physical changes in finger during verification ,system gets failed. Various fingerprint matching techniques do not authenticate wrinkled fingers. Thus error rate gets increased when matching is done between dry and wet-wrinkled fingers .Thus proposed system will extract features which will not change even after wrinkling. The proposed system will use minutiae based matching due to which error rate can be reduced. The Wet and Wrinkled Fingerprint (WWF) dataset is used to check the performance of proposed system. In this dataset there are wrinkled fingers due to wetness also some samples of dry fingers. Thus proposed matching algorithm will improve fingerprint recognition for wet fingers.
In today’s society, technology has become more advanced than the human’s mind. Companies want to make sure that their information systems stay up-to-date with the rapidly growing technology. It is very important to senior-level executives and board of directions of companies that their systems can produce the right and best information for their company to result in a greater outcome and new organizational capabilities. Big data and data analytics are one of those important factors that contribute to a successful company and their updated software and information systems.
As noted above , Big Data that is a collection of data capacity in excess of those assumed applications and traditional tools . Size of Big Data is increasing day by day , and by 2012 , it size was estimated around a few dozen terabytes to multiple petabytes ( 1 petabyte = 1024 terabytes ) only for a set of data only.
Data mining has emerged as an important method to discover useful information, hidden patterns or rules from different types of datasets. Association rule mining is one of the dominating data mining technologies. Association rule mining is a process for finding associations or relations between data items or attributes in large datasets. Association rule is one of the most popular techniques and an important research issue in the area of data mining and knowledge discovery for many different purposes such as data analysis, decision support, patterns or correlations discovery on different types of datasets. Association rule mining has been proven to be a successful technique for extracting useful information from large datasets. Various algorithms or models were developed many of which have been applied in various application domains that include telecommunication networks, market analysis, risk management, inventory control and many others
In the field of video surveillance, person re-identification is a task of matching the identity of a person captured by different cameras in the network at different places and times. The cameras used for surveillance are located at a much higher position than the person so that the conventional method of face recognition is not used for identification of the person. The images of the same person may vary from one camera view to another camera view (view point variation), or due to different lighting conditions (variation in illumination) or due to posture changes. In this paper, we introduce a re-identification method based on sparse representation. We have formulated the person re-identification as a ranking problem in which the proposed
...fman R. A. - "Data Mining and Knowledge Discovery" - A Review of issues and Multi- strategy Approach". Reports of the Machine Learning and Inference Laboratory, MCI 97-2, George Mason University, Fairfax, V.A. 1997. http://www.mli.gmu.edu/~kaufman/97-1.ps
HAND, D. J., MANNILA, H., & SMYTH, P. (2001).Principles of data mining. Cambridge, Mass, MIT Press.
Big data is a concept that has been misunderstood therefore I will be writing this paper with the intentions of thoroughly discussing this technological concept and all its dimensions with regard to what constitutes big data and how the term came about. The rapid innovations in Information Technology have brought about the realisation of big data. The concept of big data is complex and has different connotations but I intend to clarify its functions. Big data refers to the concept of a collection of large and complex amounts of data that are found extremely difficult to notate or even process by most on-hand devices and database technologies.
This white paper identifies some of the considerations and techniques which can significantly improve the performance of the systems handling large amounts of data.
The data mining process will use the mapping function which involved the decision tree and also the neural network to develop. It needs the web server and the database server to be constructed in an operating database to record the browsing route of the users. The data mining will use to identify the user’s information and classify them into different classes using decision tree.