Nec Corporation

Dotdata, Inc.

Yukitaka Kusumura

Hironori Mizuguchi

Dai Kusui

Yusuke Muraoka

Ryohei Fujimaki

Toshiyuki Kamiya

Satoshi Morinaga

Riki Eto

Kazuyo Narita

Masato Asahara

Ting Chen

Sergey Tarasenko

A descriptor generation unituses a first template prepared in advance to generate a feature descriptor, which generates a feature that may affect a prediction target from a first table including a variable of the prediction target and a second table. A feature generation unitgenerates the feature by applying the feature descriptor to the first and second tables. A feature explanation generation unitgenerates a feature explanation about the feature descriptor or the feature on the basis of a second template. An accepting unitaccepts values to be assigned to the first and second templates. The descriptor generation unitgenerates the feature descriptor by assigning the accepted values to the first template, and the feature explanation generation unitgenerates the feature explanation by assigning the values assigned to the first template to the second template.

Information processing system, feature description method and feature description program

A table acquiring meansacquires a first table including prediction objects and first attributes, and a second table including second attributes. A receiving meansreceives a similarity function and condition for similarity used to calculate the similarity between the first attribute and the second attribute. A feature generating meansgenerates feature candidates able to affect a prediction object using a combination condition for combining a record in the first table including the value of a first attribute satisfying the condition with a record in the second table including the value of a second attribute satisfying the similarity calculated with the value of the first attribute and the value of the second attribute using the similarity function, and using a reduction method for a plurality of records in the second table and a reduction condition represented by the column to be aggregated. A feature selecting meansselects an optimum feature for the prediction from the feature candidates.

Feature value generation device, feature value generation method, and feature value generation program

A prediction data input unitinputs prediction data that is one or more explanatory variables that are information likely to affect future sales. An exposure pattern generation unitgenerates an exposure pattern which is an explanatory variable indicating the content of a commercial message scheduled to be performed during a period from predicted time to future prediction target time. A component determination unitdetermines the component used for predicting the sales, on the basis of a hierarchical latent structure that is a structure in which latent variables are represented by a tree structure and components representing probability models are located at nodes of a lowest level of the tree structure, gating functions for determining a branch direction in the nodes of the hierarchical latent structure, and the prediction data and the exposure pattern. A sales prediction unitpredicts the sales on the basis of the component determined by the component determination unitand of the prediction data and the exposure pattern.

Commercial message planning assistance system and sales prediction assistance system

A table storage unitstores a first table including an objective variable and a second table different in granularity from the first table. A descriptor creation unitcreates a feature descriptor for generating a feature which is a variable that can influence the objective variable, from the first table and the second table. The descriptor creation unitcreates a plurality of feature descriptors, each by generating a combination of a mapping condition element indicating a mapping condition for rows in the first table and the second table and a reduction method element indicating a reduction method for reducing, for each objective variable, data of each column included in the second table.

Information processing system, descriptor creation method, and descriptor creation program

An enumeration plan generation unitgenerates a set of logical formula structures each representing a way of combining logical formula expressions each representing a combination of features by use of the features of learning data items and the maximum number of features to be combined, and generates partial logical formula structures by dividing a logical formula expression included in each of the generated logical formula structures into two, and generates an enumeration plan in which the partial logical formula structures are linked to the logical formula structure from which the partial logical formula structures are divided. The feature generation unitgenerates a new feature that is a combination of the features corresponding to the generated partial logical formula structures. Furthermore, the enumeration plan generation unitdivides the logical formula structure into two such that the numbers of the features included in the two partial logical formula structures generated from each of the logical formula structures are substantially equal.

Feature enumeration system, feature enumeration method and feature enumeration program

The prediction function creation device according to the present invention for creating a prediction function to derive an objective variable by using a set of samples that include explanatory variables and an objective variable, the device includes: a clustering unit that clusters the respective samples by giving labels, and assigns weights to each label in accordance with patterns of missing values for the explanatory variables in labeled samples; a child model creation unit that makes portions of the training data partial training data on the basis of the weights, and determines an explanatory variable that constitutes the prediction function on the basis of patterns of missing values for the explanatory variables in the samples; and a mixture model creation unit that creates the prediction function with respect to each pattern of missing values by using the explanatory variable and the determined partial training data.

Prediction function creation device, prediction function creation method, and computer-readable storage medium

This invention provides a water-leakage state estimation system configured to estimate a state of a water leakage in a specific area of a water distribution network. A learning unit is configured to: receive labeled data, which is labeled so as to separate past flow rate data into abnormal values and normal values, and past environment state condition data; build a prediction model for predicting the normal values in the labeled data through learning; and determine a score parameter defining a length of a period involving data to be verified through learning as well. A water-leakage estimation unit is configured to: compare predicted flow rate data obtained by supplying current environment condition data into the prediction model and current flow rate data to produce error values; and calculate an average value of the error values in the period of a window width defined by the score parameter to estimate a water-leakage score representing a state of the water-leakage in the specific area.

Water-leak state estimation system, method, and recording medium

The information processing deviceprocesses document collections having tags permitting semantic class identification appended to each document and comprises a search unit, which creates multiple semantic class units containing one, two, or more semantic classes based on a taxonomy that identifies relationships between semantic classes, and a frequency calculation unitwhich, for each of the semantic class units, identifies documents that match that semantic class unit in the document collections and, for these matching documents, calculates a first frequency that represents the frequency of occurrence in a designated document collection and a second frequency that represents the frequency of occurrence in non-designated document collections. Once the calculations have been performed, the search unitidentifies any of the semantic class units based on the first frequency and the second frequency of the matching documents.

Information processing device, information processing method, and computer-readable recording medium

In an inverted list of each node in a taxonomy, among each node, an inverted list of the highest node is a list of integer values indicating an identifier of search subject data, and an inverted list of a node other than the highest node, in place of the identifier, is a list of integer values indicating a position in an inverted list corresponding to a node that is higher by one than the node. Furthermore, a list of integer values in an inverted list of each node is divided into two or more blocks, and a differential value between an integer value and an integer value directly before the integer value in the block is converted into a bit string of a variable length integer code.

Data structure, index creation device, data search device, index creation method, data search method, and computer-readable recording medium

A boundary word identification unit () identifies a boundary word belonging to a plurality of categories among words gathered in dictionary growth processing. Then, a category membership degree calculation unit () calculates, for each category to which the boundary word belongs, a category membership degree indicating a degree to which the boundary word belongs to the category on the basis of information recorded in a gathering process memory unit (). Next, a category update unit () determines the category to which the boundary word belongs on the basis of the category membership degree calculated by the category membership degree calculation unit () and updates information stored in a gathered-by-category word memory unit () so that the determination result is reflected.

Dictionary creation device, word gathering method and recording medium

Provided are a word latent topic estimation device and a word latent topic estimation method which are capable of hierarchically performing processing and which are capable of rapidly estimating latent topics of a word while taking into consideration a mixed state of topics. The present invention is provided with: a document data addition unit () which inputs a document which includes one or more words; a level setting unit () which sets a number of topics at each level in accordance with a hierarchical structure of topics for hierarchically estimating latent topics of a word; a higher-level constraint creation unit () which, on the basis of results of topic estimation at a given level with regard to a word within the document, creates a higher-level constraint indicating an identifier of a topic for which there is a possibility of being assigned to the word and a probability of being assigned to the topic; and a higher-level-constraint-attached topic estimation unit () which, when estimating the probability of each word being assigned to each topic, refers to the higher-order constraint, uses the probability of being assigned to a parent topic at the higher level as a weight, and performs estimation processing to a lower-level topic.

Word latent topic estimation device and word latent topic estimation method

A document management & retrieval system is configured to: store, for each word in a set of words, appearance positions of the each word in a set of documents as a word index; store, for each tag in a set of tags attached to words, a set of words that appear to a right and left of the each tag, and also store, as a tag LR index, appearance positions of the each tag in a set of documents with a combination of the each tag and a word appearing to its right or a combination of the each tag and a word appearing to its left as a key; and, in a tag search where a query phrase contains words and a tag next to each other, refer to the index with a tag and the word to the right or left of the tag as a key, thereby reducing the size of a document list to be read without needing to have a tag name as a secondary key. A tag is updated by just updating two places in the tag LR index.

Document management and retrieval system and document management and retrieval method

In order to accurately learn a function for evaluating documents, even in the case where sample documents having missing feature values are included as training data, a document evaluation apparatus is provided with a data classification unit () that classifies a set of sample documents based on missing patterns of a first feature vector, a first learning unit () that uses feature values that are not missing in the first feature vector and evaluation values to learn a first function for calculating a first score which is a weighted evaluation value for each classification, a feature vector generation unit () that computes a feature value corresponding to each classification using the first score, and generates a second feature vector having the computed feature values, and a second learning unit () that uses the second feature vector and the evaluation values to learn a second function for calculating a second score for evaluating documents targeted for evaluation.

Document evaluation apparatus, document evaluation method, and computer-readable recording medium using missing patterns

A linkage information output apparatus includes: a linkage information retrieval unit for acquiring, upon receiving source information, destination information linked with the source information, a frequency of occurrence of the source information, a frequency of occurrence of linked each of the destination information, and a frequency of occurrence of a link of the source information and each of the destination information from a linkage information accumulation unit; a recognition degree calculation unit calculating, based on each acquired frequency of occurrence, a recognition degree of the source information, a recognition degree of each acquired destination information, and a recognition degree of each link; and a high interest information narrowing unit selecting destination information to output from among each destination information based on a combination of two or more among a recognition degree of the source information, a recognition degree of the destination information, and a recognition degree of the link.

Linkage information output apparatus, linkage information output method and computer-readable recording medium

Provided is a search apparatus, a search method, and a program that can improve search speed for a document set even when an object to be searched is a large-scale document set. A search apparatus, in an embodiment, includes an abstract matrix storage unit, a word frequency calculation unit, and a document frequency reference unit.

Search apparatus, search method, and non-transitory computer readable medium storing program that input a query representing a subset of a document set stored to a document database and output a keyword that often appears in the subset

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Tokyo, Japan

Yukitaka Kusumura