International Business Machines Corporation

Raymond Harry Rudy

Takayuki Osogami

Tetsuya Nasukawa

Yohei Ikawa

Miki M Enoki

Tsuyoshi Ide

Yuta Tsuboi

Yao-zhong Zhang

Marco Aurelio Stelmar Netto

Tetsuro Morimura

Satoshi Hara

Marcos Dias De Assuncao

Takashi Imamichi

Takanori Ueda

Maira Athanazio De Cerqueira Gatti

Teruo Koyanagi

Bruno Da Costa Flach

An apparatus for estimating a characteristic of a posterior distribution includes a sampling section configured to generate a plurality of samples from a prior distribution, an obtaining section configured to obtain, for each sample among the plurality of samples, a likelihood of observation given the sample, a calculation section configured to calculate a parameter relating to a density at each sample in the prior distribution, and an estimation section configured to estimate, for the plurality of samples, a characteristic of the posterior distribution based on the parameter relating to the density at each sample and the likelihood of observation for each sample.

Computational estimation of a characteristic of a posterior distribution

A method, a computer system, and/or a computer program product are disclosed. One computer-implemented method for building a hash table includes dividing a hash table into plural blocks; and dividing each block into plural sub-blocks. A certain sub-block uses a first pattern of association between a key and a location for storing the key. Another sub-block which belongs to the same block having the certain sub-block uses a second pattern which is different from the first pattern. The method may further include building a hash table by using memory blocks in a Field Programmable Gate Array.

Building of a hash table

The present invention provides at least a method includes: extracting a plurality of partial communities from a plurality of users, based on the relationships of companion messages; computing a first degree of similarity for showing the similarity of the companion partial communities, based on the relationship of a user belonging to one partial community with a user belonging to the other partial community, from among the plurality of communities; computing a second degree of similarity for showing the similarity of companion partial communities, based on words within the messages sent by users belonging to both partial communities and under the condition that the first similarity be higher than a predetermined first threshold value; and creating an integrated community by integrating the companion partial communities under the condition that the second similarity be higher than a predetermined second threshold value.

Method, computer program and computer for detecting communities in social media

The present invention provides a method for selecting an identified message group from among the plurality of messages sent by a plurality of users of social media. For the messages sent by users other than the original user that quote the message sent by the original message user, the method provides: performing a primary evaluation of the potential for being further resent; identifying a single user who has sent a message for which the primary evaluation result was higher than a predetermined first threshold; identifying a message group similar to a resent messages group, based on a resent messages group that quotes the message send by the single user; performing a secondary evaluation for the potential that the similar message group will be further resent; and selecting a message group for which the secondary evaluation result was higher than a predetermined second threshold.

Method, computer program and computer for detecting trends in social media

An analysis device which analyzes a system that inputs input data including a plurality of input parameters and outputs output data, including an acquisition unit that acquires learning data including a plurality of sets of the input data and the output data, and a learning processing unit that learns, based on the acquired learning data, the amount of difference of output data corresponding to a difference between input parameters of two pieces of input data, an analysis method using the analysis device, and a program used in the analysis device are provided.

Analysis device, analysis method, and program

Provided are a method, an apparatus and a computer program for calculating a risk assessment value for an event sequence, which are capable of calculating the risk assessment value of each even sequence by calculating a totally ordered set on the basis of a partially ordered set indicating the event sequence. The risk assessment value of an event sequence that is a partially ordered set indicating some events of an event group of M kinds of events (M is a finite natural number) in a time series. The partially ordered set is converted into an approximate totally ordered set, and an M-dimensional feature vector is calculated based on the totally ordered set obtained by the conversion. A projection matrix for calculating the risk assessment value is calculated using the calculated M-dimensional feature vector.

Calculating risk assessment value of event sequence

A method, device and computer program for efficiently identifying items having a high frequency of occurrence among items included in a large-volume text data stream. Identification information for identifying an item and a count of items are stored in a higher level of memory and only identification information is stored in a lower level. Text data stream input is received, the increment of the count of an item is increased in response to storage in the higher level memory of identification information for an item included in a bucket divided from the received text data stream input, identification information for the item is transferred with the initial count to the higher level of memory in response to storage in the lower level and the identification information for the item is newly stored with the initial count in the higher level in response to not being stored on any level.

Method, device and computer program for identifying items having high frequency of occurrence among items included in a text data stream

A method for determining an optimum policy by using a Markov decision process in which T subspaces each have at least one state having a cyclic structure includes identifying, with a processor, subspaces that are part of a state space; selecting a t-th (t is a natural number, t&#x2266;T) subspace among the identified subspaces; computing a probability of, and an expected value of a cost of, reaching from one or more states in the selected t-th subspace to one or more states in the t-th subspace in a following cycle; and recursively computing a value and an expected value of a cost based on the computed probability and expected value of the cost, in a sequential manner starting from a (t&#x2212;1)-th subspace.

Using cyclic Markov decision process to determine optimum policy

An information processing apparatus having a prediction section that determines the predicted peak usage amount of physical resources for each time interval for individual clusters each including a plurality of virtual machines having the same function; a setting section sets a constraint that ensures that, for individual combinations of a first physical machine, a second physical machine, and a time interval, the total predicted peak usage amount of a physical resource predicted for the first physical machine if the second physical machine stops during the time interval does not exceed a physical resource amount prepared for the first physical machine; and an allocation-configuration deriving section derives an allocation configuration by calculating, in accordance with the constraint, a solution to an optimization problem for minimizing, as an objective function, the sum total of the physical resource amounts of all of physical machines to which the virtual machines are allocated.

Determining an allocation configuration for allocating virtual machines to physical machines

A computer-executable method for determining an allocation configuration for allocating virtual machines to physical machines. The method includes the steps of determining a predicted peak usage amount of physical resources for each time interval for individual clusters wherein the clusters include a plurality of virtual machines; setting a constraint that ensures the total predicted peak usage amount of the physical resource predicted for the first physical machine does not exceed the physical resource amount for the first physical machine; and deriving an allocation configuration by calculating, in accordance with the constraint, a solution to an optimization problem for minimizing, as an objective function, the sum total of the physical resource amounts of the plurality of physical machines to which the virtual machines are allocated.

A weather forecast system, method of forecasting weather and a computer program product therefor. A forecasting computer applies a grid to a forecast area and provides a weather forecast for each grid cell. Population movement sensors sense population movement in the area. A swarm detector detects patterns in area population movement that indicate swarm activity, from which the swarm detector predicts swarm patterns. A planning module receives area weather forecasts and swarm patterns, and provides swarm path indications to the forecasting system for adjusting the grid applied to the forecast area.

System, method and program product for providing populace movement sensitive weather forecasts

A computer implemented method, system, and product for finding correspondence between terms in two different languages. The method includes the steps of: creating a technical term set and a general term set for each of i) a first language and ii) a second language, creating two bipartite graphs, where each graph corresponds to one of the two languages, and connects the technical term set and general term set of each language, respectively, with weighted links based on corpus information, creating a third bipartite graph by creating weighted links between general terms in the first language and general terms in the second language by using a translation dictionary, creating an association matrix M corresponding to the three bipartite graphs, calculating a similarity matrix Q by calculation of an inverse matrix, and outputting correspondence between the technical term sets of the first and second language on basis of the similarity matrix.

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Kanagawa-ken, Japan

Raymond Harry Rudy