Abstract: A method involves receiving search queries, having search terms, submitted to at least one computerized search engine. For each query, a corresponding pairwise relation in the search queries is calculated. The corresponding pairwise relation is a corresponding probability of a potential edge relationship between at least two terms. Thus, potential edges are formed. A general graph of the terms is constructed by selecting edges from the potential edges. The general graph is nodes representing the terms used in the search queries. The general graph also is edges representing semantic relationships among the nodes. A hierarchical graph is constructed from the general graph by altering at least one of the edges among the nodes in the general graph to form the hierarchical graph.

Abstract: Embodiments disclosed herein may extract trending topics from phone call transcripts or any type of text data. The phone call transcripts may be collected for a time period and the time period may be divided into time spans. For each time span having more than a threshold number of phone call transcripts, n-grams from the phone call transcripts may be extracted. The extracted n-grams may be contextually clustered by converting the n-grams into their embedding vectors, reducing the dimensionality of the embedding vectors, and clustering similar reduced dimensionality embedding vectors. Normalized occurrences of one or more clusters may be generated. The recent mean of the number of occurrences of the normalized clusters may be compared with the historical mean and offset by historical standard deviation to generate a modified Z-score. N-grams corresponding to the clusters with high Z-scores may be identified as trending topics.

Abstract: A computer-implemented method is provided to preforming re-categorization of financial transactions. The re-categorization is implemented by a server computing device which receives the financial transactions associated with a merchant and a first category. The server computing device receives user inputs that are each associated with re-categorizing a financial transaction from the first category to one or more other categories. Based at least in part on a count of the first category and counts of the one or more other categories, the server computing device determines a set of normalized ratios for the first category and the one or more other categories with respect to a total number of respective financial transactions received. The server computing device determines a second category corresponding to a minimum value in the set of the normalized ratios for each financial transaction associated with the merchant.

Abstract: A computer-implemented method is provided to preforming re-categorization of financial transactions. The re-categorization is implemented by a server computing device which receives the financial transactions associated with a merchant and a first category. The server computing device receives user inputs that are each associated with re-categorizing a financial transaction from the first category to one or more other categories. Based at least in part on a count of the first category and counts of the one or more other categories, the server computing device determines a set of normalized ratios for the first category and the one or more other categories with respect to a total number of respective financial transactions received. The server computing device determines a second category corresponding to a minimum value in the set of the normalized ratios for each financial transaction associated with the merchant.

Abstract: Aspects of the present disclosure provide techniques for predicting content relevant to questions based on reference links. Embodiments include receiving a set of question and answer (Q/A) pairs and identifying a set of references in the set of Q/A pairs that link pairs of Q/A pairs of the set of Q/A pairs. Embodiments include identifying popular Q/A pairs of the set of Q/A pairs based on the set of references. The popular Q/A pairs may be referenced by a subset of the set of Q/A pairs and each respective Q/A pair of the subset of the set of Q/A pairs may comprise a respective question of a plurality of questions. Embodiments include training a model based on the plurality of questions, the popular Q/A pairs, and the set of references, to predict Q/A pairs of the set of Q/A pairs that are relevant to a given question.

Abstract: A computer-implemented method is provided to preforming re-categorization of financial transactions. The re-categorization is implemented by a server computing device which receives the financial transactions associated with a merchant and a first category. The server computing device receives user inputs that are each associated with re-categorizing a financial transaction from the first category to one or more other categories. Based at least in part on a count of the first category and counts of the one or more other categories, the server computing device determines a set of normalized ratios for the first category and the one or more other categories with respect to a total number of respective financial transactions received. The server computing device determines a second category corresponding to a minimum value in the set of the normalized ratios for each financial transaction associated with the merchant.

Abstract: A method involves receiving search queries, having search terms, submitted to at least one computerized search engine. For each query, a corresponding pairwise relation in the search queries is calculated. The corresponding pairwise relation is a corresponding probability of a potential edge relationship between at least two terms. Thus, potential edges are formed. A general graph of the terms is constructed by selecting edges from the potential edges. The general graph is nodes representing the terms used in the search queries. The general graph also is edges representing semantic relationships among the nodes. A hierarchical graph is constructed from the general graph by altering at least one of the edges among the nodes in the general graph to form the hierarchical graph.

Abstract: Method and systems for analyzing event log elements are provided. In one example, a method includes receiving an event log element in a computer. A similarity index is calculated between the event log element and a text element. A threshold of similarity is calculated. The similarity index is compared to the threshold. If the similarity index is greater than the threshold, the event log element is grouped into a cluster with the text element to create a file of cluster assignments.

Abstract: Method and systems for analyzing event log elements are provided. In one example, a method includes receiving an event log element in a computer. A similarity index is calculated between the event log element and a text element. A threshold of similarity is calculated. The similarity index is compared to the threshold. If the similarity index is greater than the threshold, the event log element is grouped into a cluster with the text element to create a file of cluster assignments.

Abstract: Determining root causes of network issues includes identifying a previous network issue, comparing a first behavior of network components during the previous network issue with a second behavior of the network components during a current issue of a network, and obtaining a previous user input about a previous root cause of the previous network issue collected while diagnosing the previous issue.

Abstract: Stochastic based determination can include accessing a sequence of a plurality of vectors, wherein each of the plurality of vectors includes a plurality of metrics that define a performance of an application. Stochastic based determination can also include querying a digital representation of a first Stochastic Model to determine a first likelihood that the sequence will terminate in an anomalous state. Stochastic based determination can include querying a digital representation of a second Stochastic Model to determine a second likelihood that the sequence will terminate in a normal state. Stochastic based determination can include determining whether the sequence will terminate in the anomalous state based on a comparison between the first likelihood and the second likelihood.

Abstract: Determining root causes of network issues includes identifying a previous network issue, comparing a first behavior of network components during the previous network issue with a second behavior of the network components during a current issue of a network, and obtaining a previous user input about a previous root cause of the previous network issue collected while diagnosing the previous issue.

Abstract: Systems, methods, and machine-readable and executable instructions are provided for incident assignment. Incident assignment can include selecting a number of incidents that have been resolved, wherein the number of incidents are similar to a pending incident. Incident assignment can also include identifying a first number of users that resolved the number of incidents and a number of skills associated with the first number of users. Incident assignment can include creating a target user profile based on the number of skills. Incident assignment can include computing a distance from each of a second number of users to the target user profile, wherein the distance computation does not penalize the second number of users for being over qualified. Incident assignment can also include assigning the pending incident to a selected user from the second number of users wherein the selected user is selected based on the computed distances.

Abstract: Defining relationships are described. Defining relationships can include retrieving a number of event notifications that correspond to a number of nodes. Defining relationships can include defining a number of group patterns that correspond to the number of event notifications. Defining relationships can also include grouping the number of nodes into a number of groups that correlate with the number of group patterns, the number of groups defining a number of relationships between the number of nodes. Defining relationships can include assigning a number of weights to the number of relationships between the number of nodes, wherein the number of weights are based on a strength of the number of relationships between the number of nodes.