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, computer system, and computer readable product for trajectory data compression are disclosed. In embodiments, the method comprises generating spatial data for one or more moving objects; projecting the data onto a network comprised of a plurality of trajectories, the network constraining movement of the one or more moving objects; and storing the projected data in a data store. In embodiments of the invention, the method further comprises translating updates and queries to the spatial data, using specified data of the network, into links to the data store, and using the links to update and query the data store. In embodiments of the invention, the specified data of the network are stored in a network store. In embodiments of the invention, each of the trajectories includes one or more sub-trajectories, and the projecting the spatial data onto a network includes projecting the spatial data onto the sub-trajectories.

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: A method for managing a time series database, includes: monitoring multiple operations that access the time series database, so as to identify types of the multiple operations, the types of the multiple operations comprising at least one of the query types or insert types; with respect to a storage mode among multiple storage modes, obtaining costs that the multiple operations access the time series database based on the types, respectively; selecting a storage mode with the minimum cost from the multiple storage modes; and during a predetermined time period, storing into the time series database data values that are collected from multiple measurement points according to the selected storage mode. In one embodiment, there is provided an apparatus for managing a time series database. By means of the method and apparatus of the present invention, the storage and query efficiency with respect to the time series database can be increased.

Abstract: A method, computer system, and computer readable product for trajectory data compression are disclosed. In embodiments, the method comprises generating spatial data for one or more moving objects; projecting the data onto a network comprised of a plurality of trajectories, the network constraining movement of the one or more moving objects; and storing the projected data in a data store. In embodiments of the invention, the method further comprises translating updates and queries to the spatial data, using specified data of the network, into links to the data store, and using the links to update and query the data store. In embodiments of the invention, the specified data of the network are stored in a network store. In embodiments of the invention, each of the trajectories includes one or more sub-trajectories, and the projecting the spatial data onto a network includes projecting the spatial data onto the sub-trajectories.

Abstract: Processing time sequence data for multiple sensors, wherein the multiple sensors are divided into multiple sensor groups and each data comprises a time stamp and a value associated with the timestamp. The method comprises: receiving time series data from each sensor; assigning the time series data received to a sensor group to which the sensor belongs; storing time series data in a first database of a first memory, such that multiple time series data assigned to the same sensor group in the multiple sensor groups are stored in at least one database record of the first database; obtaining the time series data of each sensor among the multiple sensors from the first database; storing time series data in a second database of a second memory, such that the multiple time series data from the same sensor are stored in at least one database record of the second database.

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 method, computer system, and computer readable product for trajectory data compression are disclosed. In embodiments, the method comprises generating spatial data for one or more moving objects; projecting the data onto a network comprised of a plurality of trajectories, the network constraining movement of the one or more moving objects; and storing the projected data in a data store. In embodiments of the invention, the method further comprises translating updates and queries to the spatial data, using specified data of the network, into links to the data store, and using the links to update and query the data store. In embodiments of the invention, the specified data of the network are stored in a network store. In embodiments of the invention, each of the trajectories includes one or more sub-trajectories, and the projecting the spatial data onto a network includes projecting the spatial data onto the sub-trajectories.

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 method, computer system, and computer readable product for trajectory data compression are disclosed. In embodiments, the method comprises generating spatial data for one or more moving objects; projecting the data onto a network comprised of a plurality of trajectories, the network constraining movement of the one or more moving objects; and storing the projected data in a data store. In embodiments of the invention, the method further comprises translating updates and queries to the spatial data, using specified data of the network, into links to the data store, and using the links to update and query the data store. In embodiments of the invention, the specified data of the network are stored in a network store. In embodiments of the invention, each of the trajectories includes one or more sub-trajectories, and the projecting the spatial data onto a network includes projecting the spatial data onto the sub-trajectories.

Abstract: Processing time sequence data for multiple sensors, wherein the multiple sensors are divided into multiple sensor groups and each data comprises a time stamp and a value associated with the timestamp. The method comprises: receiving time series data from each sensor; assigning the time series data received to a sensor group to which the sensor belongs; storing time series data in a first database of a first memory, such that multiple time series data assigned to the same sensor group in the multiple sensor groups are stored in at least one database record of the first database; obtaining the time series data of each sensor among the multiple sensors from the first database; storing time series data in a second database of a second memory, such that the multiple time series data from the same sensor are stored in at least one database record of the second database.

Abstract: Processing time sequence data for multiple sensors, wherein the multiple sensors are divided into multiple sensor groups and each data comprises a time stamp and a value associated with the timestamp. The method comprises: receiving time series data from each sensor; assigning the time series data received to a sensor group to which the sensor belongs; storing time series data in a first database of a first memory, such that multiple time series data assigned to the same sensor group in the multiple sensor groups are stored in at least one database record of the first database; obtaining the time series data of each sensor among the multiple sensors from the first database; storing time series data in a second database of a second memory, such that the multiple time series data from the same sensor are stored in at least one database record of the second database.

Abstract: A method for detecting abnormal subsequences in data sequence includes constructing a hierarchical data structure of a target subsequence, each node in a bottommost layer of the data structure storing corresponding data of the target subsequence, and each node in a layer above the bottommost layer storing values based on data stored in corresponding nodes in a lower layer next to the layer above the bottommost layer; determining a second number of neighbors of the target subsequence based on the data structure of the target subsequence and of the first number of reference subsequences constructed in advance, the second number of neighbors having minimum Euclidean distances from the target subsequence; determining a third number of neighbors of each reference subsequence in the second number of reference subsequences, which have minimum Euclidean distances from each reference subsequence and determining whether the target subsequence is an abnormal subsequence.

Abstract: A method for detecting abnormal subsequences in data sequence includes constructing a hierarchical data structure of a target subsequence, each node in a bottommost layer of the data structure storing corresponding data of the target subsequence, and each node in a layer above the bottommost layer storing values based on data stored in corresponding nodes in a lower layer next to the layer above the bottommost layer; determining a second number of neighbors of the target subsequence based on the data structure of the target subsequence and of the first number of reference subsequences constructed in advance, the second number of neighbors having minimum Euclidean distances from the target subsequence; determining a third number of neighbors of each reference subsequence in the second number of reference subsequences, which have minimum Euclidean distances from each reference subsequence and determining whether the target subsequence is an abnormal subsequence.

Abstract: The present invention relates to processing of time series data. There is disclosed a method and apparatus for processing time series data, the method comprising: receiving a time series data set, wherein each element of the time series data set contains a timestamp and an original value associated with the timestamp, and times represented by all timestamps constitute a time series having fixed time intervals; converting each original value into a coded value occupying a smaller storage space, according to a predetermined monotone numerical compression coding scheme; dividing the times represented by all timestamps into a plurality of time intervals having a predetermined length; assembling coded values corresponding to all timestamps within each time interval into a data package such that the data package contains coded values arranged in an order of timestamps; and storing in a database record each data package and its associated identification of a time interval.