Abstract:
A Relational Database Management System (RDBMS) having any arbitrary structure is translated into a multi-dimensional data model suitable for performing OLAP operations upon. If a relational table defining the relational model includes any tables with cardinality of 1,1 or 0,1, the tables are merged into a single table. If the relational table is not normalized, then normalization is performed and a relationship between the original table and the normalized table is created. If the relational table is normalized, but not by dependence between columns, such as in the dimension table in a snowflake schema, the normalization process is performed using the foreign key in order to generate the normalized table. Once the normalized table is generated, OLAP measures are derived from the normalized relational table by an automated method. In addition, OLAP dimensions are derived from the normalized relational table and the results of the OLAP measures derivation by an automated method according to the present invention. According to an aspect, it is possible to associate a member of a dimension to another member of the same or another dimension. According to another aspect, it is possible to create a new dimension of analysis, the members of which are all the different values that a scalar expression can take on. According to yet another aspect, it is possible to access the various instances of a Reporting Object as members in an OLAP dimension. According to the yet another aspect, it is possible to apply opaque filters or a combination of them to the data that underlies analysis.
Abstract:
A computer readable medium is configured to receive an identification of a plurality of data records, where each data record corresponds to one of a plurality of data record schemas represented in COBOL, and each data record schema corresponds to one of a plurality of standardized data record schemas. The computer readable medium is further configured to specify one of the plurality of standardized data record schemas as a selected standardized data record schema, and to process the plurality of data records based on the selected standardized data record schema.
Abstract:
A computer readable storage medium includes executable instructions to associate two or more data sets with two or more vertices in a Venn graph. An intersection of the two or more data sets is associated with a vertex in the Venn graph. A plurality of outliers is associated between the two or more data sets and the intersection of the two or more data sets with a plurality of vertices disposed between the two or more vertices and the vertex in the Venn graph.
Abstract:
Provided is a system and method which can identify a causal relationship for anomalies in a time-series signal based on co-occurring and preceding anomalies in another time-series signal. In one example, the method may include identifying a recurring anomaly within a time-series signal of a first data value, determining a time-series signal of a second data value that is a cause of the recurring anomaly in the time-series signal of the first data value based on a preceding and co-occurring anomaly in the time-series signal of the second data value, and storing a correlation between the preceding and co-occurring anomaly in the time-series signal of the second data value and the recurring anomaly in the time-series signal of the first data value.
Abstract:
Systems and methods include determination of a first plurality of sets of data, each including values associated with respective ones of a first plurality of features, partial training of a first machine-learning model based on the first plurality of sets of data, determination of one or more of the first plurality of features to remove based on the partially-trained first machine-learning model, removal of the one or more of the first plurality of features to generate a second plurality of sets of data, partial training of a second machine-learning model based on the second plurality of sets of data, determination that a performance of the partially-trained second machine-learning model is less than a threshold, addition, in response to the determination, of the one or more of the first plurality of features to the second plurality of sets of data, and training of the partially-trained first machine-learning model based on the first plurality of sets of data.
Abstract:
Provided are a system and method which iteratively predicts an output signal of a time-series data value via execution of a time-series machine learning model on input data, decomposes the predicted output signal into a plurality of component signals corresponding to a plurality of components of the time-series machine learning model, the plurality of component signals comprising a trend signal. a cyclic signal, and a fluctuation signal, determines a plurality of global values respectively corresponding to the plurality of component signals for a first subset of the predicted output signal, where a global value is determined based on an absolute value of a respective component signal within the first subset, constructs a plurality of bars respectively corresponding to global values of the plurality of component signals, and displays the plurality of bars via a user interface.
Abstract:
Provided is a system and method which decomposes a predicted output signal of a time-series forecasting model into a plurality of sub signals that correspond to a plurality of components, and determines and displays a global contribution of each component. In one example, the method may include iteratively predicting an output signal of a time-series data value via execution of a time-series model, decomposing the predicted output signal into a plurality of component signals corresponding to a plurality of components of the time-series machine learning algorithm, respectively, and displaying the plurality of global values via a user interface.
Abstract:
An example method and system for display of out-of-window status indicators in a virtual shelf of a diagram window. A diagram framework displays a first portion of a diagram within a diagram window of a display device. The diagram comprises a set of shapes and a set of connectors representing a corresponding set of relationships between a set of objects. The framework detects that a first shape of the set of shapes at a first position of the first shape and a first status indicator associated with the first shape at a first position of the first status indicator are at least partially outside a first visible portion of the diagram within the diagram window. The diagram framework determines a second position of the first status indicator within the diagram window. The first status indicator at the second position of the first status indicator is displayed within the diagram window.
Abstract:
Anomalies may be detected using a multiple machine learning model anomaly detection framework. A clustering model is trained using an unsupervised machine learning algorithm on a historical anomaly dataset. A plurality of clusters of records are determined by applying the historical anomaly dataset to the clustering model. Then it is determined whether each cluster of the plurality of clusters is an anomaly-type cluster or a normal-type cluster. The plurality of labels for the plurality of records are updated based on the particular record's cluster classification. Non-pure clusters are determined from among the plurality of clusters based on a purity threshold. A supervised machine learning model is trained for each of the non-pure clusters using the records in the given cluster and the labels for each of those records. Then, predictions of an anomaly are made using the clustering model and the supervised machine learning models.
Abstract:
Some embodiments provide a program. The program receives a visualization collection definition specifying a plurality of visualization definitions for a plurality of visualization definitions. Each visualization definition in the plurality of visualization definitions specifies a multi-dimensional array of data definition. The program further identifies a set of multi-dimensional array of data definitions specified in the plurality of visualization definitions of the visualization collection definition. The program also sends a request for the set of multi-dimensional array of data definitions to a computing system. The program further receives the set of multi-dimensional array of data definitions from the computing system. The program also stores the set of multi-dimensional array of data definitions in a cache storage of the mobile device for later use.