62 results about "Longest common subsequence problem" patented technology

A differential compression method and computer program product combines hash value techniques and suffix array techniques. The invention finds the best matches for every offset of the version file, with respect to a certain granularity and above a certain length threshold. The invention has two variations depending on block size choice. If the block size is kept fixed, the compression performance of the invention is similar to that of the greedy algorithm, without the expensive space and time requirements. If the block size is varied linearly with the reference file size, the invention can run in linear-time and constant-space. It has been shown empirically that the invention performs better than certain known differential compression algorithms in terms of compression and speed.

The invention relates to a user classifying method and system based on mobile user trajectory similarity; the method comprises: receiving motion trajectory data of mobile users and extracting time-position information of each mobile user; using FP (frequent pattern) tree to mine a trajectory frequent sequence of the corresponding mobile user by using average stay length of the mobile user at each base station as a weight according to the time-position information; extracting according to the trajectory frequent sequence and a preset weighted support threshold to obtain a resident site of the corresponding mobile user, calculating trajectory similarity results of the mobile users through longest common subsequence algorithm according to the resident sites of the mobile users, and classifying the mobile users according to the trajectory similarity results. The FP tree is used to mine the trajectory frequent sequence of the corresponding mobile user and find the resident site of the mobile user by using the average stay length of the mobile user at each base station as a weight, user trajectory law can be guaranteed, data quantity can also be decreased, and calculation complexity is reduced.

The invention discloses a measuring system for similarity between different tracks. The system comprises a track data file uploading module and a calculation result visualization module, wherein the track data file uploading module passes a data preprocessing module and obtains a most similar section construction module, and lastly, the most similar section construction module is transmitted to a user through the calculation result visualization module. A measuring method for the measuring system for similarity between the different tracks comprises the following steps of 1.uploading data; 2.judging whether a track data file is legal, if yes, carrying out preprocessing, if no, carrying out error display; 3.evaluating similarity between the tracks through a module for calculating the similarity between the tracks; 4.searching two most similar tracks through the most similar section construction module; and 5.displaying a calculation result through the calculation result visualization module. According to the system and the method, the result is more accurate, and meanwhile, the most similar sections between the tracks are constructed by utilizing a longest common subsequence method.

The invention relates to a method for automatically extracting sentence templates which comprises the following steps that: a text is divided into a plurality of sentences according to the punctuation; serial numbers are marked in front of the sentences according to the sequence; each sentence obtained by sentence separation is divided into small blocks based on each word by using word separation technology; after the word separation is finished, the sentences are divided into a plurality of groups with ascending order or descending order according to the quantity of the words in the sentences; the sentence template can simply be obtained by applying the sentences in the same group with LCS algorithm to obtain a longest public subsequence. The invention can automatically and efficiently statisticize commonly used words and sentences from plenty of text information.

The invention provides a text similarity determination method which can improve matching accuracy. The method comprises the steps of expressing pre-determined unit names in a knowledge base with spelling; receiving text input by a user, and extracting unit names in the received text and using spelling to express the unit names; performing one-to-one matching on the unit names in the text expressed with the spelling and each unit name in the knowledge base expressed with spelling, and calculating similarity of a longest common subsequence based on spelling; sorting knowledge in the knowledge base according to the similarity of the longest common subsequence based on spelling, and selecting from the knowledge base one piece of knowledge closest to the text input by the user, wherein each piece of knowledge includes unit name. The invention relates to the field of artificial intelligence.

In response to receipt of an input string, an attempt is made to identify, in a template store, a closely matching template for use as a compression template. In response to identification of a closely matching template that can be used as a compression template, the input string is compressed into a compressed string by reference to a longest common subsequence compression template. Compressing the input string includes encoding, in a compressed string, an identifier of the compression template, encoding substrings of the input string not having commonality with the compression template of at least a predetermined length as literals, and encoding substrings of the input string having commonality with the compression template of at least the predetermined length as a jump distance without reference to a base location in the compression template. The compressed string is then output.

The invention provides a method and a system for automatically extracting virus characteristics based on family samples. According to the method and the system, a longest public subsequence algorithm is modified, a sequence A and a sequence B are established by using samples in the family samples, Hash values of subsequences with lengths equal to preset values in the sequence A and the sequence B are calculated respectively through preset feature code lengths, and the Hash values of the subsequences in the sequence A and the sequence B are matched through a red black tree manner, if the Hash values are same, the subsequences corresponding to the Hash values are public subsequences of the sequence A and the sequence B, and the public subsequences are feature codes of the family samples; and when surplus samples are taken as the sequence B and searched in a red black tree, feature codes of all family samples are obtained and combined into a feature set of the family samples, a weighting model is evaluated according to qualities of the established feature codes, the qualities of the established feature codes are judged, and the feature codes of the family samples are determined. According to the method, the time complexity of the algorithm is simplified, and the extraction efficiency and the accuracy of the feature codes are improved.

The invention relates to a text comparison method and device, a storage medium and electronic equipment. The method comprises the steps that a first text and a second text are partitioned; for a character block pair composed of the character block in the first text and the character block in the second text, the longest common sub-sequence pair of the character block pair is obtained; and according to the character information between two adjacent longest common subsequence pairs, difference description information is generated, and the difference description information is used for displayingthe character difference of the character block pair. According to the technical scheme, text comparison is carried out on the basis of the text block pairs composed of the text blocks, and the efficiency is higher than the efficiency and accuracy of text comparison with lines or words as the minimum unit.

Embodiments of the invention provide a textual similarity calculation method and a textual similarity calculation device, and an intelligent robot. The embodiments of the invention comprise the stepsof: first obtaining the longest common subsequences of two texts, then calculating the intersection and the union of the vocabulary sets corresponding to the two texts, and calculating according to the obtained intersection and union to obtain a first similarity, calculating a second similarity by using the vocabulary sets corresponding to the longest common subsequences and the previously obtained union, and finally calculating according to the first similarity and the second similarity to obtain a target similarity of the two text. The above technical solution combines the longest common subsequences and each vocabulary in the text to calculate the similarity of the two texts, thereby effectively improving the calculation accuracy of the text similarity. Progressively, chat robots or intelligent robots may provide users with more accurate answers by using accurate text similarity, which improves the service quality of the chat machines or intelligent robots, and the user experience.

A method for determining a longest common subsequence in a plurality of text strings. The method comprises: separately converting a plurality of text strings into word sequences (S100); classifying the word sequences (S400); and performing longest common subsequence computation on every class (S500). The time needed by LCS computation can be saved by classifying text strings.

The present invention discloses a hotspot aggregation method and device. The method comprises: capturing network resources on the Internet; matching the network resources by means of a longest common subsequence (LCS) algorithm to acquire matching results; and generating hotspot phrases based on the matching results. By means of the technical solutions of the present invention, the operation and maintenance cost and the complexity of hotspot aggregation calculation can be reduced, the speed of hotspot aggregation is improved, real-time acquisition and real-time calculation can be achieved, and hotspot events can be discovered fast without substantial delay.

The invention discloses a method for building an integrated enterprise process reference model based on model combination, which belongs to the field of modeling technology. The invention is characterized in that the method is realized on a platform formed by sequentially connecting a plurality of users, the Internet and servers, and an enterprise process reference model can be semi-automatically obtained by converting original enterprise models into character strings for combination, wherein the enterprise models are two modeled enterprise process models which are selected form an enterprise model database, approved by modeling experts, and put into practice. A best-fit enterprise model is obtained by the following steps of: firstly, obtaining a longest common subsequence of two process sequences; and then respectively building an auxiliary progression based on the longest common subsequence for the two process sequences; and finally obtaining the Levenshtein distance of the minimum operation time for covering one process sequence into the other process sequence by basic operation. The invention can effectively improve the reusage of enterprise model knowledge so as to effectively improve the modeling efficiency.

The invention relates to a public resource transaction data-oriented cleaning and duplicate removal method and system. The texts corresponding to public resource transaction data are stored in a dataset in a text data record form, wherein the data sets are grouped according to a preset rule, the number of the text data records in each group is controlled, and the data similarity among the text data records in each group is calculated based on the longest common subsequence. When the data similarity between the two text data records is larger than a preset threshold value, the named entity information of the two text data records is further compared, and when the named entity information of the two text data records is the same, it is judged that the two text data records belong to the repeated data, and otherwise it is judged that the two text data records belong to the non-repeated data. The repeated information in the public resource transaction data is determined in a multi-dimensional cross validation mode, so that the misjudgment of the repeated data can be further prevented on the basis of improving the text processing performance.

Systems and methods perform various optimizations of an LLCS algorithm for use in determining if a set of input sequences are similar to a query sequence. The optimizations include filtering out sequence from the set of input sequences where the estimated similarity of the sequence with the query sequence is below a threshold value. The remaining sequences can then be provided to an LLCS algorithm where the output of the LLCS algorithm is used in a similarity function to determine an actual similarity of an input sequence with a query sequence.

Disclosed herein are techniques related to automated generation of regular expressions. In some embodiments, a regular expression generator may receive input data comprising one or more character sequences. The regular expression generator may convert character sequences into a sets of regular expression codes and/or span data structures. The regular expression generator may identify a longest common subsequence shared by the sets of regular expression codes and/or spans, and may generate a regular expression based upon the longest common subsequence.

The present application relates to a method and apparatus for obtaining similar trademarks, a computer device and a storage medium. The method is based on the longest common subsequence, the longest common string, and the edit distance to determine the similarity of the character string integrity of the word mark and the prior trademark, thereby screening out similar trademarks with higher similarity and treating them again. The similarity between the word and the glyph is determined by the difference characters between the character trademark to be detected and the similarly preceding trademark. the comprehensive similarity is calculated by the overall character string judgment result and the character independent judgment result, and the prior trademark having high similarity degree is fed back to the user, the quick acquisition of the approximate trademark of the trademark to be detected is realized, and the efficiency of the approximate trademark search is improved. At the same time, the approximation judgment between the detected word mark and the prior trademark is synthesized by the overall judgment result of the string and the independent judgment result of the character. The results are determined to effectively improve the accuracy of the approximate trademark search.

The invention discloses a document classification method based on trie and LCS algorithm, comprising the following steps: step 1, pre-compiling an initial classification dictionary and an initial exclusion dictionary; 2, extending each character string in the initial classification dictionary to obtain an extended character string, filtering the obtained extended character string according to theinitial exclusion dictionary, and constructing a dictionary tree; 3, calling that dictionary tree to look up all the strings appear in each sentence in the literature to be classified, taking the longest character string in the initial classification dictionary as the longest common subsequence, and taking the longest common subsequence and its corresponding class as the final character string andfinal class of the sentence, and taking the final class which appears most frequently in a document as the class to which it belongs. The invention also discloses a document classification system based on trie and LCS algorithm. The invention omits the word segmentation process, takes the stable character string as the characteristic, has high accuracy, and reduces the dependence on the context.