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.

Abstract: A method for building indices for a time sequence in a time series database includes dividing, using a processing device, a time sequence in the time series database into a plurality of subsequences based on a sliding window; building spatial indices for the plurality of subsequences, the spatial indices being used for defining spatial locations of subsequences in the plurality of subsequences in the time sequence; and building content indices for the plurality of subsequences, the content indices being used for defining content ranges of subsequences in the plurality of subsequences.

Abstract: A method for building indices for a time sequence in a time series database includes dividing, using a processing device, a time sequence in the time series database into a plurality of subsequences based on a sliding window; building spatial indices for the plurality of subsequences, the spatial indices being used for defining spatial locations of subsequences in the plurality of subsequences in the time sequence; and building content indices for the plurality of subsequences, the content indices being used for defining content ranges of subsequences in the plurality of subsequences.

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.

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: A method for building indices for a time sequence in a time series database includes dividing, using a processing device, a time sequence in the time series database into a plurality of subsequences based on a sliding window; building spatial indices for the plurality of subsequences, the spatial indices being used for defining spatial locations of subsequences in the plurality of subsequences in the time sequence; and building content indices for the plurality of subsequences, the content indices being used for defining content ranges of subsequences in the plurality of subsequences.

Abstract: A method, apparatus and computer program product for spam message detection. The method includes collecting time domain transmission characteristic of a message source; computing frequency domain transmission characteristic of the message source with the time domain transmission characteristic of the message source; and identifying the message source to be a spammer in response to the frequency domain transmission characteristic of the message source satisfying predefined criteria; wherein the steps of the method are carried out using a computer device. An apparatus and computer program product for carrying out the above method is also provided.

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 of determining a spammer includes acquiring, by a processor, short message events associated with a user and dividing them into a plurality of sessions in terms of time; building a social network of the user in each of the sessions, and calculating at least one type of social network characteristic in each of the sessions, respectively; and determining whether the user is a spammer or not according to the social network characteristic.

Abstract: A system for determining a spammer includes a session division apparatus configured to acquire short message events associated with a user and divide them into a plurality of sessions in terms of time; a social network building and characteristic calculating apparatus configured to build a social network of the user in each of the sessions, and calculate at least one type of social network characteristic in each of the sessions, respectively; and a determination apparatus configured to determine whether the user is a spammer or not according to the social network characteristic.

Abstract: A method, apparatus and computer program product for spam message detection. The method includes collecting time domain transmission characteristic of a message source; computing frequency domain transmission characteristic of the message source with the time domain transmission characteristic of the message source; and identifying the message source to be a spammer in response to the frequency domain transmission characteristic of the message source satisfying predefined criteria; wherein the steps of the method are carried out using a computer device. An apparatus and computer program product for carrying out the above method is also provided.

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.