Data have become valuable assets for enterprises.Data governance aims to manage and reuse data assets,facilitating enterprise management and enabling product innovations.A data lineage graph(DLG)is an abstracted colle...Data have become valuable assets for enterprises.Data governance aims to manage and reuse data assets,facilitating enterprise management and enabling product innovations.A data lineage graph(DLG)is an abstracted collection of data assets and their data lineages in data governance.Analyzing DLGs can provide rich data insights for data governance.However,the progress of data governance technologies is hindered by the shortage of available open datasets for DLGs.This paper introduces an open dataset of DLGs,including the DLG model,the dataset construction process,and applied areas.This real-world dataset is sourced from Huawei Cloud Computing Technology Company Limited,which contains 18 DLGs with three types of data assets and two types of relations.To the best of our knowledge,this dataset is the first open dataset of DLGs for data governance.This dataset can also support the development of other application areas,such as graph analytics and visualization.展开更多
Several Wireless Fidelity(WiFi)fingerprint datasets based on Received Signal Strength(RSS)have been shared for indoor localization.However,they can’t meet all the demands of WiFi RSS-based localization.A supplementar...Several Wireless Fidelity(WiFi)fingerprint datasets based on Received Signal Strength(RSS)have been shared for indoor localization.However,they can’t meet all the demands of WiFi RSS-based localization.A supplementary open dataset for WiFi indoor localization based on RSS,called as SODIndoorLoc,covering three buildings with multiple floors,is presented in this work.The dataset includes dense and uniformly distributed Reference Points(RPs)with the average distance between two adjacent RPs smaller than 1.2 m.Besides,the locations and channel information of pre-installed Access Points(APs)are summarized in the SODIndoorLoc.In addition,computer-aided design drawings of each floor are provided.The SODIndoorLoc supplies nine training and five testing sheets.Four standard machine learning algorithms and their variants(eight in total)are explored to evaluate positioning accuracy,and the best average positioning accuracy is about 2.3 m.Therefore,the SODIndoorLoc can be treated as a supplement to UJIIndoorLoc with a consistent format.The dataset can be used for clustering,classification,and regression to compare the performance of different indoor positioning applications based on WiFi RSS values,e.g.,high-precision positioning,building,floor recognition,fine-grained scene identification,range model simulation,and rapid dataset construction.展开更多
Malicious webshells currently present tremendous threats to cloud security.Most relevant studies and open webshell datasets consider malicious webshell defense as a binary classification problem,that is,identifying wh...Malicious webshells currently present tremendous threats to cloud security.Most relevant studies and open webshell datasets consider malicious webshell defense as a binary classification problem,that is,identifying whether a webshell is malicious or benign.However,a fine-grained multi-classification is urgently needed to enable precise responses and active defenses on malicious webshell threats.This paper introduces a malicious webshell family dataset named MWF to facilitate webshell multiclassification researches.This dataset contains 1359 malicious webshell samples originally obtained from the cloud servers of Alibaba Cloud.Each of them is provided with a family label.The samples of the same family generally present similar characteristics or behaviors.The dataset has a total of 78 families and 22 outliers.Moreover,this paper introduces the human–machine collaboration process that is adopted to remove benign or duplicate samples,address privacy issues,and determine the family of each sample.This paper also compares the distinguished features of the MWF dataset with previous datasets and summarizes the potential applied areas in cloud security and generalized sequence,graph,and tree data analytics and visualization.展开更多
基金the National Natural Science Foundation of China(No.62272480 and 62072470)。
文摘Data have become valuable assets for enterprises.Data governance aims to manage and reuse data assets,facilitating enterprise management and enabling product innovations.A data lineage graph(DLG)is an abstracted collection of data assets and their data lineages in data governance.Analyzing DLGs can provide rich data insights for data governance.However,the progress of data governance technologies is hindered by the shortage of available open datasets for DLGs.This paper introduces an open dataset of DLGs,including the DLG model,the dataset construction process,and applied areas.This real-world dataset is sourced from Huawei Cloud Computing Technology Company Limited,which contains 18 DLGs with three types of data assets and two types of relations.To the best of our knowledge,this dataset is the first open dataset of DLGs for data governance.This dataset can also support the development of other application areas,such as graph analytics and visualization.
基金National Natural Science Foundation of China(No.42001397)National Key Research and Development Program of China(No.2016YFB0502102)+2 种基金Introduction and Training Program of Young Creative Talents of Shandong Province(No.0031802)Doctoral Research Fund of Shandong Jianzhu University(No.XNBS1985)National College Student Innovation and Entrepreneurship Training Program(No.S202110430036).
文摘Several Wireless Fidelity(WiFi)fingerprint datasets based on Received Signal Strength(RSS)have been shared for indoor localization.However,they can’t meet all the demands of WiFi RSS-based localization.A supplementary open dataset for WiFi indoor localization based on RSS,called as SODIndoorLoc,covering three buildings with multiple floors,is presented in this work.The dataset includes dense and uniformly distributed Reference Points(RPs)with the average distance between two adjacent RPs smaller than 1.2 m.Besides,the locations and channel information of pre-installed Access Points(APs)are summarized in the SODIndoorLoc.In addition,computer-aided design drawings of each floor are provided.The SODIndoorLoc supplies nine training and five testing sheets.Four standard machine learning algorithms and their variants(eight in total)are explored to evaluate positioning accuracy,and the best average positioning accuracy is about 2.3 m.Therefore,the SODIndoorLoc can be treated as a supplement to UJIIndoorLoc with a consistent format.The dataset can be used for clustering,classification,and regression to compare the performance of different indoor positioning applications based on WiFi RSS values,e.g.,high-precision positioning,building,floor recognition,fine-grained scene identification,range model simulation,and rapid dataset construction.
基金the National Natural Science Foundation of China(No.62272480 and 62072470).
文摘Malicious webshells currently present tremendous threats to cloud security.Most relevant studies and open webshell datasets consider malicious webshell defense as a binary classification problem,that is,identifying whether a webshell is malicious or benign.However,a fine-grained multi-classification is urgently needed to enable precise responses and active defenses on malicious webshell threats.This paper introduces a malicious webshell family dataset named MWF to facilitate webshell multiclassification researches.This dataset contains 1359 malicious webshell samples originally obtained from the cloud servers of Alibaba Cloud.Each of them is provided with a family label.The samples of the same family generally present similar characteristics or behaviors.The dataset has a total of 78 families and 22 outliers.Moreover,this paper introduces the human–machine collaboration process that is adopted to remove benign or duplicate samples,address privacy issues,and determine the family of each sample.This paper also compares the distinguished features of the MWF dataset with previous datasets and summarizes the potential applied areas in cloud security and generalized sequence,graph,and tree data analytics and visualization.
