Abstract: The system, method, and computer program product described herein provide unified real-time rule analytics to users through the use of an analytics logic editor that allows a user to construct an analytic logic rule unit that may be used on both edge and cloud devices. The user may select a data source, transform, rule condition, and action using an interface of the analytics logic editor to construct an analytics logic rule unit that may be deployed to both edge and cloud devices in real-time without the need to separately program each device. The analytics logic rule unit may be installed and executed by the edge and cloud device in real-time.

Abstract: The system, method, and computer program product described herein provide unified real-time rule analytics to users through the use of an analytics logic editor that allows a user to construct an analytic logic rule unit that may be used on both edge and cloud devices. The user may select a data source, transform, rule condition, and action using an interface of the analytics logic editor to construct an analytics logic rule unit that may be deployed to both edge and cloud devices in real-time without the need to separately program each device. The analytics logic rule unit may be installed and executed by the edge and cloud device in real-time.

Abstract: A computer-implemented method is provided that includes receiving a search request to perform a search in a time series database, where the time series database stores a plurality of sequences of measured values arranged in chronological order, extracting from the search request a condition for performing the search in the time series database, the condition including a target subsequence to be searched for within the plurality of sequences of measured values within the time series database, selecting a search algorithm to perform the search from a plurality of search algorithms registered to the time series database, based on one or more characteristics of the condition and historical statistic data of one or more historical searches conducted on the time series database, and obtaining a search result from the time series database, utilizing the search algorithm.

Abstract: A method for processing a time series includes dividing, with a processing device, the time series into a plurality of windows by time; extracting at least one group of similar subsequences from a current window among the plurality of windows; and updating a candidate list on the basis of comparison between similar subsequences in each group of the at least one group with k characteristic subsequences in the candidate list; wherein the k characteristic subsequences are k characteristic subsequences with a greatest number of occurrences in at least processed parts of the time series.

Abstract: A method for processing a time series includes dividing, with a processing device, the time series into a plurality of windows by time; extracting at least one group of similar subsequences from a current window among the plurality of windows; and updating a candidate list on the basis of comparison between similar subsequences in each group of the at least one group with k characteristic subsequences in the candidate list; wherein the k characteristic subsequences are k characteristic subsequences with a greatest number of occurrences in at least processed parts of the time series.

Abstract: A plurality of messages comprising a stream of sensed data is received from an Internet of Things (IoT) device. A plurality of rules is identified. Responsive to a received message of the plurality of messages, the plurality of rules are distributed into a rule index tree based upon a cost model which minimizes a cost for analyzing the plurality of rules to determine whether or not each of the plurality of rules can be applied to the received message. The rule index tree is used to identify a set of rules of the plurality of rules that can be applied to the received message. The set of rules is applied to the received message to generate an outgoing message.

Abstract: The present disclosure relates to methods and systems for storing and querying data. According to the embodiments of the present invention, two-layer indexes are created for multi-dimension data, wherein the primary index is created based on two or more dimensions to retrieve respective data units of the data, while the secondary index is created based on specific dimensions to retrieve respective data blocks in the data unit. Correspondingly, when receiving a multi-dimension query request for data, the primary retrieval first determines a data unit including the target data based on a primary index, and then the secondary retrieval quickly locates a data block including the target data based on the secondary index. In this way, the multi-dimension retrieval can be efficiently performed. Moreover, by appropriately setting the size of a smallest data block, the I/O efficiency of data access will be significantly enhanced.

Abstract: The present disclosure relates to methods and systems for storing and querying data. According to the embodiments of the present invention, two-layer indexes are created for multi-dimension data, wherein the primary index is created based on two or more dimensions to retrieve respective data units of the data, while the secondary index is created based on specific dimensions to retrieve respective data blocks in the data unit. Correspondingly, when receiving a multi-dimension query request for data, the primary retrieval first determines a data unit including the target data based on a primary index, and then the secondary retrieval quickly locates a data block including the target data based on the secondary index. In this way, the multi-dimension retrieval can be efficiently performed. Moreover, by appropriately setting the size of a smallest data block, the I/O efficiency of data access will be significantly enhanced.

Abstract: The system, method, and computer program product described herein provide unified real-time rule analytics to users through the use of an analytics logic editor that allows a user to construct an analytic logic rule unit that may be used on both edge and cloud devices. The user may select a data source, transform, rule condition, and action using an interface of the analytics logic editor to construct an analytics logic rule unit that may be deployed to both edge and cloud devices in real-time without the need to separately program each device. The analytics logic rule unit may be installed and executed by the edge and cloud device in real-time.

Abstract: The system, method, and computer program product described herein provide unified real-time rule analytics to users through the use of an analytics logic editor that allows a user to construct an analytic logic rule unit that may be used on both edge and cloud devices. The user may select a data source, transform, rule condition, and action using an interface of the analytics logic editor to construct an analytics logic rule unit that may be deployed to both edge and cloud devices in real-time without the need to separately program each device. The analytics logic rule unit may be installed and executed by the edge and cloud device in real-time.

Abstract: A plurality of messages comprising a stream of sensed data is received from an Internet of Things (IoT) device. A plurality of rules is identified. Responsive to a received message of the plurality of messages, the plurality of rules are distributed into a rule index tree based upon a cost model which minimizes a cost for analyzing the plurality of rules to determine whether or not each of the plurality of rules can be applied to the received message. The rule index tree is used to identify a set of rules of the plurality of rules that can be applied to the received message. The set of rules is applied to the received message to generate an outgoing message.

Abstract: A computer-implemented method is provided that includes receiving a search request to perform a search in a time series database, where the time series database stores a plurality of sequences of measured values arranged in chronological order, extracting from the search request a condition for performing the search in the time series database, the condition including a target subsequence to be searched for within the plurality of sequences of measured values within the time series database, selecting a search algorithm to perform the search from a plurality of search algorithms registered to the time series database, based on one or more characteristics of the condition and historical statistic data of one or more historical searches conducted on the time series database, and obtaining a search result from the time series database, utilizing the search algorithm.

Abstract: A computer-implemented method for searching in a database is provided according to one embodiment. The method includes, in response to receiving a search request to search in a database, extracting from the search request a condition for searching in the database. The method further includes selecting a search algorithm matching the condition from a plurality of search algorithms registered to the database, based on historical statistic data of historical search conducted on the database. Moreover, the method includes obtaining a search result from the database by using the search algorithm. The database is a time series database.

Abstract: A computer-implemented method of processing a data query, includes in an edge device, processing a subquery of the data query, storing first statistical data on the subquery, and analyzing the first statistical data to optimize a parameter for processing subqueries.

Abstract: A computer-implemented method for searching in a database is provided according to one embodiment. The method includes, in response to receiving a search request to search in a database, extracting from the search request a condition for searching in the database. The method further includes selecting a search algorithm matching the condition from a plurality of search algorithms registered to the database, based on historical statistic data of historical search conducted on the database. Moreover, the method includes obtaining a search result from the database by using the search algorithm. The database is a time series database.

Abstract: Data processing device and method. The device includes: a spreadsheet of data displaying row for displaying a part of data retrieved from a database and a hyper row for expressing the remaining data; a data processor configured to calculate the value of the formula based on the data retrieved from the database. According to the device and method of the present invention, it is possible to eliminate overhead for loading data from the database to the spreadsheet when there are massive data records, continuously update the resulting data, and minimize users' development and migration cost.

Abstract: The present disclosure relates to methods and systems for storing and querying data. According to the embodiments of the present invention, two-layer indexes are created for multi-dimension data, wherein the primary index is created based on two or more dimensions to retrieve respective data units of the data, while the secondary index is created based on specific dimensions to retrieve respective data blocks in the data unit. Correspondingly, when receiving a multi-dimension query request for data, the primary retrieval first determines a data unit including the target data based on a primary index, and then the secondary retrieval quickly locates a data block including the target data based on the secondary index. In this way, the multi-dimension retrieval can be efficiently performed. Moreover, by appropriately setting the size of a smallest data block, the I/O efficiency of data access will be significantly enhanced.

Abstract: A method for processing a time series includes dividing, with a processing device, the time series into a plurality of windows by time; extracting at least one group of similar subsequences from a current window among the plurality of windows; and updating a candidate list on the basis of comparison between similar subsequences in each group of the at least one group with k characteristic subsequences in the candidate list; wherein the k characteristic subsequences are k characteristic subsequences with a greatest number of occurrences in at least processed parts of the time series.

Abstract: A method for processing a time series includes dividing, with a processing device, the time series into a plurality of windows by time; extracting at least one group of similar subsequences from a current window among the plurality of windows; and updating a candidate list on the basis of comparison between similar subsequences in each group of the at least one group with k characteristic subsequences in the candidate list; wherein the k characteristic subsequences are k characteristic subsequences with a greatest number of occurrences in at least processed parts of the time series.

Abstract: The present disclosure relates to methods and systems for storing and querying data. According to the embodiments of the present invention, two-layer indexes are created for multi-dimension data, wherein the primary index is created based on two or more dimensions to retrieve respective data units of the data, while the secondary index is created based on specific dimensions to retrieve respective data blocks in the data unit. Correspondingly, when receiving a multi-dimension query request for data, the primary retrieval first determines a data unit including the target data based on a primary index, and then the secondary retrieval quickly locates a data block including the target data based on the secondary index. In this way, the multi-dimension retrieval can be efficiently performed. Moreover, by appropriately setting the size of a smallest data block, the I/O efficiency of data access will be significantly enhanced.