Ndsu-research Foundation

North Dakota State University

William K Perrizo

Victor T Shi

Amal Shehan Perera

Taufik Fuadi Abidin

Masum Serazi

Systems and methods for performing data mining in a set of binary data arranged as a plurality of data items in which each data item has a plurality of bits, each bit in a corresponding one of a plurality of bit positions. The set of binary data is arranged in the data storage such that the binary data is in bit position groups. Each bit position group corresponds to a different one of the plurality of bit positions and includes bits of the binary data having that bit position. The binary data of each bit position group is compressed to produce data structures representing the set of binary data. A data mining technique is performed using the plurality of compressed data structures.

System and method for organizing, compressing and structuring data for data mining readiness

A system and method for performing and accelerating cluster analysis of large data sets is presented. The data set is formatted into binary bit Sequential (bSQ) format and then structured into a Peano Count tree (P-tree) format which represents a lossless tree representation of the original data. A P-tree algebra is defined and used to formulate a vertical set inner product (VSIP) technique that can be used to efficiently and scalably measure the mean value and total variation of a set about a fixed point in the large dataset. The set can be any projected subspace of any vector space, including oblique sub spaces. The VSIPs are used to determine the closeness of a point to a set of points in the large dataset making the VSIPs very useful in classification, clustering and outlier detection. One advantage is that the number of centroids (k) need not be pre-specified but are effectively determined. The high quality of the centroids makes them useful in partitioning clustering methods such as the k-means and the k-medoids clustering. The present invention also identifies the outliers.

Method and system for data mining of very large spatial datasets using vertical set inner products

Systems and methods of classifying a subject data item based on a training set of pre-classified data items. A piecewise-linear approximation of a local boundary between different classes of data items is automatically computed. The local boundary is approximated by a neighborhood set of data items selected from the training set that have been pre-classified into different classes and have points similar to a point of the subject data item. A class is automatically assigned to the subject data item in accordance with a side of the local boundary on which the subject data item resides.

Parameter optimized nearest neighbor vote and boundary based classification

A system and method for concurrency control in high performance database systems. Generally includes receiving a database access request message from a transaction. Then, generating an element that corresponds to the access request message. The element type is that of a read element, commit element, validated element, or restart element. The element is then posted to a read-commit (RC) queue. If the element is a commit element, an intervening validation of the transaction is performed. Upon the transaction passing validation the requested database access is performed.

Multiversion read-commit order concurrency control

Concurrency control in high performance database systems

A system and method to take data, which is in the form of an n-dimensional array of binary data where the binary data is comprised of bits that are identified by a bit position within the n-dimensional array, and create one file for each bit position of the binary data while maintaining the bit position identification and to store the bit with the corresponding bit position identification from the binary data within the created filed. Once this bit-sequential format of the data is achieved, the formatted data is structured into a tree format that is data-mining-ready. The formatted data is structured by dividing each of the files containing the binary data into quadrants according to the bit position identification and recording the count of 1-bits for each quadrant on a first level. Then, recursively dividing each of the quadrants into further quadrants and recording the count of 1-bits for each quadrant until all quadrants comprise a pure-1 quadrant or a pure-0 quadrant to form a basic tree structure.

Growing community of inventors

Fargo, ND, United States of America

William K Perrizo