Abstract: Systems and methods for generating a storage policy for a storage system are provided. The method comprises receiving a target function applicable to a storage system having one or more data storage mediums, wherein the target function represents values for storage parameters associated with productivity or loss tolerance in the storage system; implementing one or more simulation rules according to the received target function; generating one or more storage operation requests to access data on said one or more data storage mediums based on said one or more simulation rules; submitting said one or more storage operation requests to the storage system for processing; analyzing simulation results obtained for the storage system, in response to the storage system processing said one or more storage operation requests; and generating one or more storage policies, by a machine learning entity, in response to analyzing the simulation results.

Abstract: Determining a network transmitter that is more likely to cause handoff failures in a telecommunication service based on historical data records. The historical data records may be standard Call Data Records. A probability that a service provided by a first network transmitter will be handoffed to a second network transmitter is determined. An indication that a target network transmitter is overly busy is determined based on the number of failed services for each network transmitter that may handoff a service the target network transmitter and the probability that a service will be handoffed to the target network transmitter. Based on the indication, measures may be taken to increase quality level of the target network transmitter.

Abstract: Data records of a service provider may be utilized to estimate data regarding to users who are customers of an alternative service provider, such as a competitor. The data records may indicate interaction between users. An estimated value of a selected user may be determined based on a statistical model. The statistical model may be built using training data. The statistical model may take into account social activity of the selected user, such as which users are socially proximate to him. The statistical model may take into account interactions of the selected user with users who are customers of the service provider. The statistical model may take into account demographic data. The statistical model may take into account data regarding users who are socially proximate to the selected user.

Abstract: A method and system are provided for maintaining profiles of information channels available on the Web, wherein the information channels are accessed via pull-only protocols. The method includes monitoring one or more channels by a channel pull action at a monitoring rate, wherein the monitoring rate is determined for the one or more channels based on the number of update events in a previous time period. The method may optimally include filtering the update events in the time period by a novelty measure, wherein the filtering disregards events that do not include significant novel information. The monitoring rate is adapted based on reinforcement learning applying iterative learning rules over time.

Abstract: A computer implemented method for generating a storage policy for a storage system based on simulation results associated with a state of the storage system is provided. The method comprises receiving a target function applicable to a storage system, wherein the target function represents a measure of values associated with storage parameters related to productivity and loss tolerance of the storage system; wherein the simulation results for a state of the storage system are calculated based on a least one of (a) the storage system simulated response to a set of simulated file-related storage operation requests generated based on one or more simulation rules, (b) the state of the storage system before responding to the set of simulated file-related storage operation requests, (c) the storage system target function; and (d) rules for simulating file-related storage operation requests.

Abstract: A novel and useful mechanism enabling a standard learning algorithm to generate rules for complex event processing (CEP) systems. The method creates rules that infer previously defined output events by creating input event feature vectors for each targeted output event. In addition, a method for automatically generating CEP system rules to infer output events which are anomalies (i.e. statistical outliers) of input event sequences is disclosed. Input feature vectors consisting of multiple input events and parameters for each targeted output event are then input into a standard learning algorithm to generate CEP system rules.

Abstract: A method, system, and computer program product for popularity prediction of user-generated content are provided. The method includes measuring the novelty of a user-generated content and predicting the popularity of the user-generated content based on the measured novelty. Predicting the popularity of the user-generated content includes: extracting basic features of the user-generated content; measuring novelty features of the user-generated content; and predicting the popularity based on the basic features and novelty features.

Abstract: Churn prediction is performed by monitoring quality of service levels provided to customers. A time in which the customer is due to either churn or renew his agreement with the service provider may be monitored or computed. Machine learning methods may be utilized to determine a probability of churn based on historic data. Based upon the determination an output to retention personnel may be provided and an improved offer may be made to customers that are deemed in risk of churning.

Abstract: A method and system for using social bookmarks wherein a social bookmark is a triplet of the entities of user, document, and tag. The method including: collecting multiple bookmarks; representing the bookmarks as a three-dimensional space or matrix of the number of times a user u, used tag t to bookmark document d; measuring the similarity of two entities of the same type; and using the similarity to weight bookmarks or entities. The weightings may be used to provide a measure of a usefulness of a bookmark for describing a document for retrieval purposes. Two-dimensions of the bookmark space may also be used to predict the third-dimension.

Abstract: A method and system are provided for maintaining profiles of information channels available on the Web, wherein the information channels are accessed via pull-only protocols. The method includes monitoring one or more channels by a channel pull action at a monitoring rate, wherein the monitoring rate is determined for the one or more channels based on the number of update events in a previous time period. The method may optimally include filtering the update events in the time period by a novelty measure, wherein the filtering disregards events that do not include significant novel information. The monitoring rate is adapted based on reinforcement learning applying iterative learning rules over time.

Abstract: An advanced metering infrastructure comprises intermediate nodes. The intermediate nodes receive data from child nodes and aggregate the data according to groups of child nodes. The aggregation provides for a reduced version of the data. The reduction is performed based on groups determined by clustering. The reduced version of the data comprises data describing a group of measurements over time, such as a centroid in an n-dimension space, number of customers in the group, radius of the group and the like. The centroid may shift over time based on a consumption profile, such as low consumption at noon, and high consumption at evening. The consumption profiles may be determined in a learning phase, as well as shifting of centroids of each group over time.

Abstract: An advanced metering infrastructure comprises intermediate nodes. The intermediate nodes receive data from child nodes and relay a subset of the data that is not according to an expected value. The expected value may be determined based on a forecasting function computed based on past data. The expected value may be a spatial shape in an n-dimension space. A data not within the spatial shape may be considered not in accordance with the expected value. In some case, the spatial shape is defined by a centroid and a radius. The spatial shape may shift over time based on a consumption profile, such as low consumption at noon, and high consumption at evening. The consumption profiles may be determined in a learning phase, as well as shifting of spatial shapes of each group over time.

Abstract: A method and system are provided of merging results in distributed information retrieval. A search manager is in communication with a plurality of components, wherein a component is a search engine working on a document collection and returning results in the form of a list of documents to a search query. The search manager submits a query to the plurality of components, receives results from each component in the form of a list of documents; estimates the success of a component in handling the query to generate a merit score for a component per query; applies the merit score to the results for the component; and merges results from the plurality of components by ranking in order of the applied merit score.

Abstract: A method and system are provided for maintaining profiles of information channels available on the Web, wherein the information channels are accessed via pull-only protocols. The method includes monitoring one or more channels by a channel pull action at a monitoring rate, wherein the monitoring rate is determined for the one or more channels based on the number of update events in a previous time period. The method may optimally include filtering the update events in the time period by a novelty measure, wherein the filtering disregards events that do not include significant novel information. The monitoring rate is adapted based on reinforcement learning applying iterative learning rules over time.

Abstract: A method and system of resource allocation for execution of a job are provided. The method includes receiving feedback (134) regarding the execution of previously submitted jobs on one or more resource nodes (101-104), and estimating the resources required for execution of a submitted job based on the feedback (134) and the parameters of the job. One, or a plurality of resource nodes in parallel, having the estimated resources are allocated the job. The feedback may be implicit feedback indicating the success or failure of the execution of a job. The one or more resource nodes (101-104) allocated for execution of a job may have less than a user requested resource allocation for the job.

Abstract: A computer implemented method for generating a storage policy for a storage system based on simulation results associated with a state of the storage system is provided. The method comprises receiving a target function applicable to a storage system, wherein the target function represents a measure of values associated with storage parameters related to productivity and loss tolerance of the storage system; wherein the simulation results for a state of the storage system are calculated based on a least one of (a) the storage system simulated response to a set of simulated file-related storage operation requests generated based on one or more simulation rules, (b) the state of the storage system before responding to the set of simulated file-related storage operation requests, (c) the storage system target function; and (d) rules for simulating file-related storage operation requests.

Abstract: A method for generating a storage policy, the method includes: receiving a storage system target function; and generating, by a machine learning entity, the storage policy in response to: (a) a set of file-related storage operation requests, (b) a state of the storage system before responding to the set of file-related storage operation requests, and (c) the storage system target function. A method for evaluating a storage policy, the method includes: simulating an application of the storage policy by the storage system during a first period, in response to a set of file-related storage operation requests that was provided to the storage system during the first period, to provide a simulation result; wherein the first period starts before the simulating.

Abstract: A social reference group of a set of customers may be determined based on the interaction of customers. Using a portion of database of a service provider, interactions between customers may be determined. The interactions are analyzed to provide an at least one social reference group. A social reference group comprises a portion of the set of customers that are deemed socially similar. A behavior of a customer may be predicted based on past interactions and/or properties of the social reference group. The predicted behavior may be prevented by performing an action such as for example offering a customer about to churn an improved deal.

Abstract: Determining a network transmitter that is more likely to cause handoff failures in a telecommunication service based on historical data records. The historical data records may be standard Call Data Records. A probability that a service provided by a first network transmitter will be handoffed to a second network transmitter is determined. An indication that a target network transmitter is overly busy is determined based on the number of failed services for each network transmitter that may handoff a service the target network transmitter and the probability that a service will be handoffed to the target network transmitter. Based on the indication, measures may be taken to increase quality level of the target network transmitter.

Abstract: A method and system for analyzing a document set (202, 420) are provided. The method includes determining a set of terms (312) from the terms of the document set that minimizes a distance measurement (405) from the given set of documents (420). The method includes using a greedy algorithm to build the set of terms incrementally, at each stage finding a single word that is closest to the document set (202, 420). The set of terms is evaluated to assess the ability to find the document set (202, 420). The set of terms are compared with expected terms to evaluate the ability to find the document set (202, 420). A measure of the ability to find a document set (202, 420) is provided by computing a distance measure (403) between a document set and an entire collection.