Abstract: A method of encoding knowledge is disclosed, which can be used to automatically detect problems in software application deployments. The method includes accessing a source of knowledge describing a problem known to occur in deployments of a particular software application, and which identifies a plurality of conditions associated with the problem. An encoded representation of the knowledge source is generated according to a predefined knowledge encoding methodology. The encoded representation is adapted to be applied automatically by a computer to analyze data representing a current state of a monitored deployment of the software application to detect whether the conditions and the problem exist therein.

Abstract: A situational decision engine (SDE) operates within a mobile device or server to present a user with display content specifically optimized to the user's present situation. By acquiring characteristics about the user's present situation, the SDE is capable of analyzing the general state of the user and make decisions about presenting information with situational relevance. With the SDE, promoters and marketing firms may target user incentives and present the incentives based on a correlation of incentive features and the users context. After acquiring key characteristics of a user's context, the SDE analyzes and determines an optimal correlation to available incentives. By optimizing the matching of a promotion, an ad, or a survey to the user's situation, maximum benefit to both user and the promoter may be realized. By analyzing a user's context and presenting display content accordingly, the SDE is able to optimize the user's experience.

Abstract: A method for recommending items includes forming one or more choice sets of recommendable items. At least one choice set is a personalized choice set of recommendable items formed for a first user based in part on data associated with that user. Recommendable items can be divided into classification groups. Forming a choice set of recommendable items can be based on a user's classification group preferences. The method also includes forming a recommendation set of recommendable items from the choice sets of recommendable items. Forming the recommendation set is based in part on additional data associated with the user. The additional data is not used in forming the personalized choice set.

Abstract: An apparatus for making probabilistic inferences based on a belief network includes a processing system configured to receive as input one or more parameters of a causal influence model. The belief network has a child node Y and one or more parent nodes Xi (i=1, . . . , n) for the child node Y. The causal influence model describes the influence of the parent nodes Xi on possible states of the child node Y. The processing system is further configured to use a creation function to convert the parameters of the causal influence model into one or more entries of a conditional probability table. The conditional probability table provides a probability distribution for all the possible states of the child node Y, for each combination of possible states of the parent nodes Xi.

Abstract: A rule stored in a tangible computer-readable memory is adapted to be programmatically applied by a computer to automatically detect occurrences of a particular problem in a deployment of a software application. The rule comprises a plurality of atomic gates and a plurality of operator gates. The atomic gates have predicates for detecting occurrences of a plurality of conditions in the software application deployment. The operator gates are configured to detect predefined logical combinations of outputs of the atomic gates to generate a rule output that indicates whether the problem is currently detected in the deployment. The rule contains a plurality of free logical variables which can be assigned values. The free logical variables are configured for use in input pattern matching and output binding substitutions to supply information to a remedy for the problem. Each substitution comprises an assignment of a value to a variable.

Abstract: An information system based on time, space and relevance is provided. The system comprises a client that displays information in a user-friendly manner and is located in a domestic environment; a proxy that handles the collection and parsing of data; a server that gathers usage data from the client; a database data mining cluster that allows for user profiling and time, space and relevance analysis; a set of information channels, which are periodically updated, and upon which automatic suggestions are given based on a user profile.

Abstract: A method and a system identify information placed on a good to be processed, and the way of processing the good depends on this information. With regard to the method, the following steps are performed reading the information by an identifier and speaking the information as a voice message into an information retrieval system; delivering the voice message to a plurality of voice recognition systems; analyzing the voice message in each of the plurality of voice recognition systems and identifying a preliminary result; evaluating the preliminary results utilizing conditional probabilities of correct recognition in order to identify the most probable result among the preliminary results best matching the initial information; and assigning the most probable result to the respective good. The system provides a reliable, high-speed multi speech recognition even in high ambient noise environments without the cost and complexity of introducing an OCR.

Abstract: A system is provided for managing a data structure based variable rules engine. The system includes a processor, a set of questions, a variables data structure comprising values and a set of variables, a results data structure comprising a set of requirements, a user interface, and a rules engine. The user interface outputs the set of questions and receives responses to the set of questions. The rules engine, when executed by the processor, assigns results to the set of variables by combining the responses with the values in the variables data structure, and determines whether the results meet the set of requirements in a results data structure. The set of questions, values, responses, and results are associated with a plurality of time frames. The user interface also outputs the results for the plurality of time frames in response to a determination that the results meet the set of requirements.

Abstract: The present disclosure provides an automated method for the detection and identification of money service business transactions, including: performing a preprocessing operation, wherein the preprocessing operation includes filtering a dataset; performing a feature extraction operation, wherein the feature extraction operation includes extracting predetermined features from a transaction signal; performing a statistical analysis operation for the testing of significance of extracted features and dimension reduction; and performing one or more of a nonlinear classification operation and a linear classification operation, wherein the nonlinear or linear classification operation includes classifying data that appears to be related to a money service business transaction.

Abstract: In certain example embodiments of this invention, there is provided systems and/or methods for processing BAM-related data to predict when events of interest are about to happen and/or to identify the root causes of, or at least data correlated with, such events of interest. In certain example embodiments, key performance indicators (KPIs) are gathered and gardened. The gardening process may identify KPI values of interest (e.g., based on a Z-factor analysis thereof across one or more collection intervals). The gardened KPIs may be processed using a time-series transform (e.g., a Fast Fourier Transform), matched to one of a plurality of predefined waveforms, and fed into a dynamic Naïve Bayesian Network (NBN) for prediction. The gardened data also may be used to determine the relevance of the KPI for root causes of problems (e.g., based on a chi-square analysis).

Abstract: Systems and methods for solving a constraint satisfaction problem (CSP) defined by one or more variables, constraints and probabilistic requests (PRs) are provided. In one embodiment, each PR is mapped onto a set of constraints that implement the PR. The CSP solver then finds a series of solutions to the CSP. For each solution, if a constraint associated with a PR is satisfied, then a success counter for that PR is incremented; else a failure counter for that PR is incremented. If the constraint was not considered for the purpose of solving the CSP, then the failure counter for the PR associated with the constraint is incremented.

Abstract: A system and a method are disclosed for implementing managing relationships between data on a mobile computing based on user creation and definition of the relationships. A mobile computing device user generates asymmetric relationships between stored entities, events, tasks and communications. These asymmetric relationships are custom-generated by the user to reflect the user's classification and evaluation of the relationships. The user also assigns a score to each relationship and stored entity, event, task and communication to represent the user's classification of the relationship, entity, event, task or communication. Hence, the user assigns a value to the relationships between data as well as the stored data to indicate which relationship or stored data is most valuable to the user. The user-defined relationships between data can be automatically displayed when the mobile communication device is used to interact with a stored entity.

Abstract: A clustering method yields a searchable hierarchy to speed retrieval, and can function dynamically with a changing document population. Nodes of the hierarchy climb up and down the emerging hierarchy based on locally sensed information. Like previous ant clustering algorithms, the inventive process is dynamic, decentralized, and anytime. Unlike them, it yields a hierarchical structure. For simplicity, and reflecting our initial application in the domain of textual information, the items being clustered are documents, but the principles may be applied to any collection of data items.

Abstract: Exemplary non-transitory computer-readable medium, method and system for providing at least one recommendation to users and applications using multidimensional data. The multidimensional data can define a multidimensional space defined by a Cartesian product of the dimensions. The multidimensional space can have at least three dimensions, and each of the dimensions can be capable of (i) providing variable information, and (ii) having a type that is different from a type of another one of the dimensions. The exemplary non-transitory computer-readable medium, method and system can retrieve information from data associated with the multidimensional space. Further, the exemplary non-transitory computer-readable medium, method and system can generate the at least one recommendation based on the retrieved information. Further, at least one of the dimensions can include profiles.

Abstract: A decision-making method, a decision-making system, and a PDF are disclosed herein. The decision-making method comprises: receiving application session data that carries access network data and access user data; and making policy and charging decisions according to the application session data. Through the decision-making method, decision-making system, and PDF, the application session data comprises access network data and access user data of the UE so that the PCRF can uniquely determine a user according to the access network data or the access user data and the IP address allocated by the network to the UE, and make policy and charging decisions.

Abstract: A method and apparatus for managing configurations of computer resources in a datacenter is described. In one embodiment, a method comprises analyzing multiple configurations using rule information to produce an analysis result where each configuration in the multiple configurations defines a configuration of a resource that is managed by the data center, training a Bayesian classifier using the analysis result, and classifying a second configuration using the trained Bayesian classifier.

Abstract: A load fuzzy modeling device for the power system based on the causality diagram, the composite cloud generator and the improved T-S fuzzy model, comprising: a hardware device, including: the sensors, a signal conditioning and filter module, an A/D conversion module, a causality conversion module, a DSP module, a memory module, a display and keyboard module connected with the DSP module, respectively and a communication module connected with the PC.

Abstract: A method and system for automatically classifying a product description into a product type based on a classification system is disclosed. Real product descriptions representing a given product type can be compared with a pre-defined set of characteristics that are relevant to the classification system. The relevant information regarding the classification system is extracted from each of the real product descriptions. The extracted information can then be coalesced and transformed into a new set of classification rules for a new product type. The new classification rules can be used to automatically classify the product descriptions that represent the new product type. The classification system can be a rule based classification system and/or semantic web classification system.

Abstract: A method for predicting cycle time comprises the steps of: collecting a plurality of known sets of data; using a clustering method to classify the known sets of data into a plurality of clusters; using a decision tree method to build a classification rule of the clusters; building a prediction model of each cluster; preparing data predicted set of data; using the classification rule to determine that to which clusters the predicted set of data belongs; and using the prediction model of the cluster to estimate the objective cycle time of the predicted set of data. Therefore, engineers can beforehand know the cycle time that one lot of wafers spend in the forward fabrication process, which helps engineers to properly arrange the following fabrication process of the lot of wafer.

Abstract: When transmitting an on-road location on a digital map, an information provider transmits, as on-road location information, string of coordinates information representing the road shape of a road section including the on-road location having a predetermined length, additional information including at least one information item chosen from a group of the attribute information on the road including the road location and detailed information on the nodes in the road section, and relative information indicating the on-road location in the road section. Receiving party receiving this on-road location information performs shape matching to identify the road section on a digital map and uses relative data to identify the on-road location in this road section. The receiving party can identify the road shape with high accuracy even when only a small amount of string of coordinates data is available. This makes it possible to reduce the data transmission amount and identify an on-load location with high accuracy.