Adobe, Inc.

Manoj Awasthi

Nitin Goel

Kapil Malik

Balaji Krishnamurthy

Shagun Sodhani

Suraj Satishkumar Sheth

Rohit Bajaj

Harsh Rathi

Embodiments of the present invention relate to efficiently computing variable predictiveness such that an indication of variable predictiveness can be provided in real time. In this regard, aspects of the present invention enable a user (e.g., digital marketer) to input a query and, in response, receive an indication of variable predictiveness. To efficiently compute variable predictiveness in response to a submitted user query, mutual information is computed offline and, thereafter, used to generate, in real time, conditional mutual information of variables for a specified date range. The concept of conditional mutual information can be utilized to represent variable predictiveness or otherwise indication variable predictiveness, such as to identify a set of variables that accurately predict a metric. Using such an approach effectively reduces a number of data access attempts and calculations performed in real time thereby reducing utilization of a processor(s).

Efficient computation of variable predictiveness

In some embodiments, a processor accesses a metrics dataset, which includes metrics whose values indicate data network activity. The metrics dataset has segments. Each segment is a respective subset of the data items having a common feature. The processor identifies anomalous segments in the metrics dataset. Each anomalous segment has a segment trend that is different from a trend associated with the larger metrics dataset. The processor generates a data graph that includes nodes, which represent anomalous segments, and edges connecting the nodes. The processor applies weights to the edges. Each weight indicates (i) a similarity between a pair of anomalous segments represented by the nodes connected by the weighted edge and (ii) a relationship between the anomalous segments and the metrics dataset. The processor ranks the anomalous segments based on the applied weights and selects one or more segments with sufficiently high ranks.

Identifying significant anomalous segments of a metrics dataset

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Ghaziabad, India

Manoj Awasthi