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    • 7. 发明专利
      AU2021203512B2 Interpretation of a dataset 未知
    • Interpretation of a dataset
    • AU2021203512B2
    • 2022-05-26
    • AU2021203512
    • 2021-05-28
    • TATA CONSULTANCY SERVICES LTD
    • AGARWAL PUNEET , SHROFF GAUTAM , SAIKIA SARMIMALA , SRINIVASAN ASHWIN
    • G06F17/00
    • INTERPRETATION OF A DATASET A method and a system for interpreting a dataset comprising a plurality of items is described herein. The method may include computing a rule set pertaining to the dataset, generating a rule cover, calculating a plurality of distances between the plurality of rule pairs in the rule cover and generating a distance matrix based on the calculated plurality of distances between the plurality of rule pairs, storing the calculated plurality of distances between the plurality of rule pairs, clustering the overlapping rules within the rule cover using the distance matrix; selecting a representative rule from each cluster, determining at least one exception for each representative rule in the rule cover selected from each cluster and interpreting the dataset using the representative rules and the at least one exception determined for each representative rule in the rule set. To be published with Fig. 1 104-1 DATA INTERPRETATION SYSTEMlQZ 104-2 PROCESSOF(S)l1 INTERFACE{S)12 MODULE(S) 11 RULE GENERATION MODULE 120 102 NETWORK 102l INTERPRETATIONMODULE122 OTHER MODULE(S) 124 N 104-N DATAl11t RULE SET DATA121 INTERPRETATION DATA 12F O THER DAT A13
    • 基本信息 添加关注 加入工作空间 PDF全文 PDF下载 全文下载 相似专利 双屏查看 权利要求书 说明书全文 Espacenet 法律信息 同族专利 专利引用
    • 8. 发明专利
      AU2019201857B2 Sparse neural network based anomaly detection in multi-dimensional time series 未知
    • Sparse neural network based anomaly detection in multi-dimensional time series
    • AU2019201857B2
    • 2020-10-15
    • AU2019201857
    • 2019-03-18
    • TATA CONSULTANCY SERVICES LTD
    • MALHOTRA PANKAJ , GUGULOTHU NARENDHAR , VIG LOVEKESH , SHROFF GAUTAM
    • G06F11/07
    • SPARSE NEURAL NETWORK BASED ANOMALY DETECTION IN MULTI DIMENSIONAL TIME SERIES Anomaly detection from time series is one of the key components in automated monitoring of one or more entities. Domain-driven sensor selection for anomaly detection is restricted by knowledge of important sensors to capture only a certain set of anomalies from the entire set of possible anomalies. Hence, existing anomaly detection approaches are not very effective for multi-dimensional time series. Embodiments of the present disclosure depict sparse neural network for anomaly detection in multi-dimensional time series (MDTS) corresponding to a plurality of parameters of entities. A reduced-dimensional time series is obtained from the MDTS via an at least one feedforward layer by using a dimensionality reduction model. The dimensionality reduction model and recurrent neural network (RNN) encoder-decoder model are simultaneously learned to obtain a multi-layered sparse neural network. A plurality of error vectors corresponding to at least one time instance of the MDTS is computed to obtain. An anomaly score. [To be published with FIG. 2] receiving, at an input layer, a multi-dimensional time series corresponding to a plurality of parameters of an entity 202 obtaining, using a dimensionality reduction model, a reduced-dimensional time series from the multi-dimensional time series via an at least one feedforward layer, wherein 204 connections between the input layer and the feedforward layer are sparse to access at least a portion of the plurality of parameters estimating, by using a recurrent neural network (RNN) encoder-decoder model, the multi-dimensional time 206 series using the reduced-dimensional time series obtained by the dimensionality reduction model simultaneously learning, by using the estimated multi-dimensional time series, the dimensionality reduction model and the RNN encoder-decoder model to obtain a multi-layered sparse neural network computing, by using the multi-layered sparse neural network, a plurality of error vectors corresponding to at least onetime instance of the multi-dimensional time series by 210 performing a comparison of the multi-dimensional time series and the estimated multi-dimensional time series generating at least one anomaly score based on the plurality of the error vectors 212
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