Abstract: A tensor data processing method is provided. The method comprises receiving an input tensor including at least one of an outlier and a missing value, the input tensor being input during a time interval between a first time point and a second time point, factorizing the input tensor into a low rank tensor to extract a temporal factor matrix, calculating trend and periodic pattern from the extracted temporal factor matrix, detecting the outlier which is out of the calculated trend and periodic pattern, updating the temporal factor matrix except the detected outlier, combining the updated temporal factor matrix and a non-temporal factor matrix of the input tensor to calculate the real tensor and recovering the input tensor by setting data corresponding to a position of the outlier or a position of the missing value of the input tensor from the data of the real tensor as an estimated value.

Abstract: Various embodiments may provide an electronic device for incremental lossless summarization of a dynamic massive graph and an operating method thereof. In the electronic device and the operating method thereof according to various embodiments, a summary graph created from a massive graph and the differences between the massive graph and the summary graph may be stored, a changed edge may be detected from the massive graph, changed nodes connected by the changed edge may be detected based on the changed edge, and the summary graph and the edge corrections may be updated based on each of the changed nodes.

Abstract: Computer-implemented techniques for lossless and lossy summarization of large-scale graphs. Beneficially, the lossless summarization process is designed such that it can be performed in a parallel processing manner. In addition, the lossless summarization process is designed such that it can be performed with having to store only a certain small number of adjacency list node objects in-memory at once and without having to store an adjacency list representation of the entire input graph in-memory at once. In some embodiments, the techniques involve further summarizing the reduced graph output from the lossless summarization process in a lossy manner. Beneficially, the lossy summarization process uses a condition that is computationally efficient to evaluate when determining whether to drop edges of the reduced graph while at the same time ensuring the accuracy of a graph restored from the lossy reduced graph compared to the input graph is within the error bound.