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Bar-ilan University

Roy Gelbard

Ran Bittmann

A system and methods are provided for dynamic classifying in real time cases received in a stream of big data. The system comprises multiple remote autonomous classifiers. Each autonomous classifier generates a classification scheme comprising a plurality of classifier parameters. Upon receiving a case, the system determines from among the plurality of classifier parameters a most similar classifier parameter and the case may be added to a buffer of cases represented by the most similar classifier parameter. When a measure of error between the case and the most similar classifier parameter is greater than a threshold, the buffer is dynamically regrouped into one or more new buffers, according to a criterion of segmentation quality. One or more new classifier parameters, representing the one or more regrouped case buffers, are added to the classification scheme.

Method and system for dynamic updating of classifier parameters based on dynamic buffers

A method and system for sharing trusted contact information between trusted, known and unknown parties, without revealing the contact information itself, thus protecting the party defined in the contact from possible unsolicited messages known as 'Spam'. Addresses of trusted senders are encrypted using a one-way encryption and stored in a whitelist repository that can be shared by different users. When a message is received, its senders address is extracted, encrypted using the same encryption method, and compared if it is found in the whitelist repository.

Method and system for sharing trusted contact information

An association method for analyzing a dataset and grouping its objects in homogeneous groups. Multiple grouping algorithms of the art are performed against the dataset, each producing its own grouping results. All the distributions (groupings) are ordered in a matrix, where each distribution suggested by a specific algorithm and/or parameters is presented in a column of the matrix, and each object is presented in a row of the matrix. The association method is applied to the grouping results and associates similar groups among different grouping methods. The best grouping of the objects is determined according to one or more quality criteria. The various grouping results are represented by colors in the matrix such that similar colors, even on different rows, represent similar grouping recommendations.

Method for efficient association of multiple distributions

A system and method for enhancing bitmap indexing representation of a dataset, which comprises a plurality of cases and features, each case characterized by one or more values of each feature. Currently, the bins vector for each case in the dataset, is a binary array, which is a bitmap indexing representation of each respective feature of the case. The system and method enhance the bitmap indexing by padding each bins vector. The padding is carried out by identifying all target bit locations with a '1' value and replacing at least one '0' bit adjacent to a target bit location with a non-zero numerical value, thereby creating a padded bitmap index. The padding factor may be based on any mathematical or statistical factor concerning population or subpopulation relevant to each of the features of the dataset.

Method and system for extended bitmap indexing

An information system (IS) architecture comprising an application environment operative to develop, run and edit on the fly any IS application, and a database environment coupled to the application environment and including a generic and rigid schema operative to represent all business and application components of any application in the application environment as records of the generic and rigid schema. The generic and rigid schema provides an on the fly capability to change any business component or application component in a run time application, thereby leaving the schema and the application environment unchanged during system operation.

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Tel Aviv, Israel

Roy Gelbard