A high-efficiency and precise spam message interception method, device and system

Abstract

The application discloses a kind of high-efficient and accurate junk message interception method, equipment and system, belong to communication technical field.Method includes: synchronously recent year's mobile phone number of entering network as blacklist and storage;Length screening is carried out to be issued short message first, and the short message of length≤40 bytes is directly issued;The verification whether the sending number of the short message of length>40 bytes is in blacklist is not in then, is issued, and is carried out short message content similarity matching in then;Similarity is greater than preset threshold X then, intercepts and updates junk message content library, otherwise, is issued;Similarity is calculated by the ratio of set intersection and union.This application further discloses the equipment and system for realizing this method.This application significantly improves the efficiency and accuracy of junk message interception, reduces the processing pressure of short message center, and has low deployment cost and strong sustainability, and can be widely applied to junk message management of operator short message center.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a highly efficient and accurate method, device, and system for intercepting spam text messages. Background Technology

[0002] With the rapid development of mobile communication technology, SMS, as a convenient and low-cost communication method, has been widely used in personal communication, business promotion, and other scenarios. However, at the same time, the proliferation of spam SMS has caused great annoyance to users, not only disrupting their normal lives but also potentially spreading harmful information, committing fraud, and other illegal and irregular activities, thus endangering the security of the communication environment. Among these, point-to-point spam SMS is the main method of sending spam SMS, characterized by its dispersed delivery, diverse content, and strong concealment, posing a significant challenge to interception efforts.

[0003] Globally, spam text messages are a widespread problem. In my country, the fight against spam text messages mainly relies on the government's mandatory real-name registration policy for mobile phone SIM cards. By increasing the difficulty of obtaining SIM cards and raising the cost of sending spam text messages, the number of spam text messages decreased for a certain period. However, this approach is not based on technological innovation and lacks sustainability. Over time, on the one hand, users' sensitivity and responsiveness to text messages have continued to increase, leading to a sustained increase in the profit potential of sending spam text messages; on the other hand, criminals are using various means to circumvent real-name registration supervision, resulting in a resurgence of spam text messages and bringing new pressure to the governance of telecommunications networks.

[0004] Currently, the mainstream method used by telecom operators to combat spam SMS messages mainly relies on user complaints to obtain the content of spam messages, and then extracting keywords from these complaints to build a spam keyword database. When the SMS center receives each message to be sent, it matches the message content against all keywords in the keyword database. If any keyword is matched, the message is considered spam and its delivery is blocked. If no match is found after completing all keyword matching, the message is sent as a legitimate message.

[0005] Existing keyword-matching-based spam SMS blocking methods have core shortcomings in terms of efficiency and accuracy, as detailed below: 1. Low SMS sending efficiency, impacting SMS center performance: As the types and quantity of spam SMS messages continue to increase, the keyword database will continue to expand. Each SMS message to be sent needs to be compared with all keywords in the keyword database one by one. The comparison process is time-consuming, which seriously delays the SMS sending speed. Especially during peak SMS sending periods, this will cause a surge in processing pressure on the SMS center, resulting in SMS congestion, delivery delays, and other problems. This reduces the overall operating performance of the SMS center and affects the user's SMS receiving experience.

[0006] 2. High false positive and false negative rates, and poor interception accuracy: Keyword extraction is extremely difficult. On the one hand, if a normal SMS message happens to contain a keyword from the keyword database, it will be falsely identified as spam, preventing the normal SMS from being delivered and infringing on the user's right to communication. On the other hand, criminals can easily bypass the keyword matching mechanism by simply transforming keywords (such as splitting, replacing, using homophones, adding irrelevant characters, etc.), allowing spam SMS messages to be successfully delivered, resulting in false negatives and making it impossible to effectively intercept spam SMS messages. In addition, existing technologies do not consider the inherent characteristics of spam SMS messages and rely solely on single keyword matching, further reducing the accuracy of interception. Summary of the Invention

[0007] In view of the above-mentioned defects in the existing technology, the purpose of this invention is to provide a highly efficient and accurate method, device and system for intercepting spam text messages. By mining the inherent characteristics of spam text messages and combining them with similarity matching algorithms, this invention solves the problems of low efficiency and high false blocking and missed blocking rates of existing methods, achieves rapid identification and accurate interception of spam text messages, and at the same time ensures the efficient delivery of normal text messages, thereby improving the performance of the SMS center and the user experience.

[0008] A highly efficient and accurate method for blocking spam text messages includes the following steps: (1) Synchronization of blacklisted numbers: The SMS center synchronizes the list of mobile phone numbers that have been registered in the past year from the operator's customer relationship management (CRM) system at a fixed time every day, and uses this list as the blacklisted numbers; (2) Blacklist storage: The blacklist numbers are inserted into the database of the computer server according to the preset data format to establish a blacklist index for real-time query; (3) Preliminary screening of SMS length: The SMS center calculates and judges the length of each SMS to be sent. If the length of the SMS content is less than or equal to 40 bytes, the SMS is identified as a normal SMS and sent immediately; if the length of the SMS content is greater than 40 bytes, the next step is taken. (4) Sending number verification: Extract the MO (Mobile Originator) of the SMS to be sent, i.e., the sender's mobile phone number, and query the database index to see if the MO number exists in the blacklist; if the query result is that it does not exist, send the SMS immediately; if the query result is that it exists, proceed to the next step. (5) SMS content similarity matching: The content of the SMS to be sent is compared with the content of each historical spam SMS in the spam SMS content database. If the similarity between a historical spam SMS and the SMS to be sent is greater than the preset threshold X, the SMS to be sent is identified as spam, blocked and prevented from being sent, and the SMS to be sent is added to the spam SMS content database. If the similarity is not greater than the threshold X after matching with all historical spam SMS in the spam SMS content database, the SMS is identified as a normal SMS and sent normally. (6) Similarity calculation method: The similarity S between the two text messages is calculated using the following formula: S=|S1∩S2| / |S1∪S2|; where S1 is the set of each Chinese character as an element in the first text message, and S2 is the set of each Chinese character as an element in the second text message; S1∩S2 is the intersection of S1 and S2, and |S1∩S2| is the number of elements in the intersection S1∩S2; S1∪S2 is the union of S1 and S2, and |S1∪S2| is the number of elements in the union S1∪S2; (7) Threshold adjustment: The threshold X ranges from 0.5 to 0.9 and is dynamically adjusted according to the actual application scenario of false blocking and missed blocking. When a normal SMS is falsely blocked, the value of X is increased; when spam SMS is missed, the value of X is decreased.

[0009] Furthermore, the fixed time mentioned in step (1) is 00:00 every day. The synchronous operation is completed after establishing a communication connection with the operator's CRM system through a preset interface, which is an HTTP interface.

[0010] Furthermore, the database mentioned in step (2) is a high-performance relational database, including but not limited to MySQL and Oracle. The database supports fast queries with a query response time of no more than 10ms, and the database is equipped with a regular automatic backup mechanism.

[0011] Furthermore, the spam SMS content database mentioned in step (5) consists of historically intercepted spam SMS messages and spam SMS messages reported by users, and the spam SMS content database is updated regularly to ensure the timeliness and comprehensiveness of the samples.

[0012] Furthermore, the initial value of the threshold X in step (7) is set to 0.7. After 1-2 weeks of trial operation, it is fine-tuned according to the actual interception data to achieve the optimal interception accuracy.

[0013] A highly efficient and accurate spam SMS interception device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements all the steps of the aforementioned spam SMS interception method.

[0014] Furthermore, it also includes a communication interface and a backup module.

[0015] A highly efficient and accurate spam SMS interception system includes an SMS receiving module, a blacklist synchronization module, a database storage module, a length filtering module, a number verification module, a similarity matching module, an SMS delivery / interception module, a threshold adjustment module, a spam SMS content library, and a CRM system interface. The modules are connected and work together through a communication bus. The SMS receiving module is used to receive all SMS messages to be sent in the mobile communication network and send the SMS content, MO number, and receiving time information to the length filtering module. The CRM system interface is used to establish a communication connection between the blacklist synchronization module and the operator's CRM system, providing support for the synchronization of the mobile phone number list; The blacklist synchronization module is used to synchronize a list of mobile phone numbers that have joined the network in the past year through the CRM system interface at 00:00 every day, generate a blacklist, and send the blacklist to the database storage module. The database storage module is used to store relevant data of blacklisted mobile phone numbers and spam SMS content database, establish data indexes, and provide real-time query support for number verification module and similarity matching module; The length filtering module is used to receive SMS information sent by the SMS receiving module, calculate the length of the SMS content, and if the length is ≤40 bytes, send a sending instruction to the SMS sending / interception module; if the length is >40 bytes, send the SMS information to the number verification module. The number verification module is used to extract the MO number from the SMS message, query the blacklist in the database storage module, and if the number is not in the blacklist, send a delivery instruction to the SMS delivery / interception module; if the number is in the blacklist, send the SMS message to the similarity matching module. The similarity matching module is used to extract the SMS content from the SMS information, perform similarity calculation with the spam SMS content library in the database storage module, and send the calculation result to the SMS delivery / interception module. The SMS delivery / interception module is used to receive instructions from the length filtering module, the number verification module, and the similarity matching module, perform delivery operations on normal SMS messages, perform interception operations on spam SMS messages, and add the intercepted spam SMS messages to the spam SMS content library. The threshold adjustment module is used to dynamically adjust the similarity threshold X based on the interception data of the SMS delivery / interception module, including the number of false blocks and the number of missed blocks, and send the adjusted threshold to the similarity matching module. The spam SMS content database is used to store historically blocked spam SMS messages and spam SMS messages reported by users, providing sample support for the similarity matching module and updating the sample data regularly.

[0016] The beneficial effects of this invention are as follows: 1. Significantly Improved Interception Efficiency: By initially filtering SMS messages by length and verifying the MO number, most legitimate SMS messages are quickly eliminated, reducing the workload of subsequent similarity matching. This avoids the tedious process of "comparing all keywords one by one" in traditional keyword matching, significantly shortening SMS interception processing time, increasing SMS delivery speed, and reducing the processing pressure on the SMS center, ensuring its stable operation. Actual testing shows that the SMS processing speed of this invention is more than 60% faster than existing technologies, meeting the real-time processing needs of massive amounts of SMS messages.

[0017] 2. Significantly improved interception accuracy: The similarity matching method replaces the traditional keyword matching, effectively avoiding the problem of missed interceptions caused by keyword deformation; at the same time, by dynamically adjusting the similarity threshold, the contradiction between false blocking and missed blocking is balanced, reducing the probability of false blocking of normal SMS messages.

[0018] 3. High practicality and low deployment cost: The method, equipment and system of this invention are compatible with the existing SMS center and CRM system of operators, without the need for large-scale modification, and the deployment difficulty and cost are low; at the same time, blacklist synchronization, threshold adjustment and other operations can be completed automatically without manual intervention, which reduces operating costs and is suitable for large-scale promotion and application.

[0019] 4. High sustainability: Through continuous updates to the spam SMS content database and dynamic adjustments to thresholds, this invention can adapt to changes in spam SMS content and sending methods, maintaining a highly efficient and accurate interception effect over the long term. This solves the problems of existing technologies being "easily circumvented and lacking sustainability," providing reliable technical support for purifying the communication network environment. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating the overall process of spam SMS interception in this invention. Figure 2 This is a flowchart of the blacklist synchronization and storage process of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0023] This invention, through statistical analysis and in-depth research of a large amount of historical spam SMS data, uncovers two core inherent characteristics of spam SMS. Combined with a similarity matching algorithm, it constructs a multi-dimensional interception system to achieve efficient and accurate interception. The specific principle is as follows: 1. Content length characteristics of spam text messages: Analysis of tens of thousands of historical spam text message samples revealed that spam text messages are mostly commercial promotions, scams, and harassment messages. In order to convey sufficient misleading information or advertising content, their content length is usually greater than 20 Chinese characters (if it contains letters, numbers, etc., the corresponding length is greater than 40 bytes). In contrast, normal text messages are mostly personal communication messages, with concise content and a length that usually does not exceed the above threshold. This characteristic can be used as a preliminary screening criterion for spam text messages, quickly eliminating most normal text messages and improving interception efficiency.

[0024] 2. Characteristics of Spam Message Sending Numbers: To evade regulation and reduce costs, spammers typically avoid using long-term mobile phone numbers and instead use newly registered numbers acquired within the last few months. These numbers are often temporary and can be quickly cancelled after sending spam messages, reducing the risk of detection. Based on this characteristic, a blacklist can be created by filtering recently registered mobile phone numbers, enabling initial interception of spam message sending numbers.

[0025] 3. SMS Similarity Matching Principle: To address the issues of false blocking and missed blocking caused by keyword matching, this invention designs an SMS similarity matching method. This method calculates the similarity between the SMS to be blocked and SMS messages in a historical spam SMS content database, and determines whether it is a spam SMS based on a similarity threshold. This method eliminates the need for keyword extraction, effectively avoiding missed blocking caused by keyword variations. Furthermore, by setting a reasonable similarity threshold, it reduces false blocking of legitimate SMS messages, improving blocking accuracy. The similarity calculation uses the ratio of the intersection to the union of sets, accurately reflecting the degree of similarity between two SMS messages. The calculation process is simple and efficient, suitable for the real-time processing needs of SMS centers.

[0026] Based on the above principles, the spam SMS interception method of the present invention is implemented in the following steps, which are executed sequentially to ensure the efficiency and continuity of the interception process: 1. Blacklist Number Synchronization: Every day at 00:00 (midnight), the SMS center establishes a communication connection with the operator's CRM (Customer Relationship Management) system through a preset interface to synchronize a list of mobile phone numbers that have registered for the network in the past year. This list is then used as a blacklist of suspected spam SMS senders. The limitation of "the past year" is because extensive data verification shows that spam SMS senders typically have a usage period of no more than one year. Numbers older than one year are mostly used by legitimate users, effectively reducing redundancy in the blacklist and lowering query pressure. The synchronization time is chosen to be 00:00, when SMS sending volume is at its lowest, avoiding interference with the normal operation of the SMS center.

[0027] 2. Blacklist Storage: The blacklisted phone numbers obtained in step 1 are inserted into the computer server's database according to a preset data format (such as phone number strings, network access timestamps, etc.), creating a blacklist index for subsequent real-time queries. The database uses a high-performance relational database (such as MySQL, Oracle, etc.), supporting fast query operations and ensuring that the number query response time does not exceed 10ms, meeting the requirements for real-time SMS delivery. Simultaneously, the database is equipped with a regular backup mechanism to prevent blacklist data loss and ensure the continuity of interception operations.

[0028] 3. Initial SMS Length Screening: After receiving each SMS message to be sent, the SMS center first calculates its content length. If the message content length is less than or equal to 40 bytes (or less than or equal to 20 Chinese characters if it only contains Chinese characters), the message is directly considered normal and sent immediately without further interception. If the message content length is greater than 40 bytes, it proceeds to the next step for further evaluation. This step quickly filters out most normal SMS messages, reducing subsequent processing pressure and improving interception efficiency.

[0029] 4. Sending Number Verification: Extract the MO (Mobile Originator, i.e., the sender's mobile phone number) of the SMS to be sent, and query the database index to see if the number exists in the blacklist stored in step 2; if the query result is that the number does not exist, it means that the number is not a recently registered suspected spam SMS sending number, and the SMS is identified as a normal SMS and sent immediately; if the query result is that the number exists, it means that the number is a suspected spam SMS sending number, and proceed to the next step to perform content similarity matching.

[0030] 5. SMS Content Similarity Matching: The content of the SMS to be sent is compared with the content of each historical spam SMS in the spam SMS content database. If the similarity between a historical spam SMS and the SMS to be sent is greater than a preset threshold X, the SMS to be sent is identified as spam, immediately blocked, and its delivery is prevented. Simultaneously, the SMS is added to the spam SMS content database to enrich the sample library. If, after matching with all historical spam SMS in the spam SMS content database, the similarity is not greater than the threshold X, the SMS is identified as a normal SMS and is sent normally. The spam SMS content database consists of historically blocked spam SMS and spam SMS reported by users, and is updated regularly to ensure the timeliness and comprehensiveness of the sample.

[0031] 6. Similarity Calculation Method: The similarity S between two SMS messages is calculated using the following formula: S = |S1∩S2| / |S1∪S2|; where S1 is the set of each Chinese character in the first SMS message, and S2 is the set of each Chinese character in the second SMS message; S1∩S2 is the intersection of S1 and S2, i.e., the set of elements (Chinese characters) that exist in both S1 and S2; S1∪S2 is the union of S1 and S2, i.e., the set of elements (Chinese characters) that exist in S1, S2, or both; |S1∩S2| is the number of elements in the intersection S1∩S2, and |S1∪S2| is the number of elements in the union S1∪S2. This calculation method is simple and efficient, can quickly reflect the similarity between two SMS messages, and is not affected by keyword variations, effectively solving the problem of missed blocking.

[0032] 7. Similarity Threshold Adjustment: The threshold X ranges from 0.5 to 0.9 and can be dynamically adjusted based on the actual application scenarios of false blocking and missed blocking. When legitimate SMS messages are falsely blocked, it indicates that the threshold is set too low, and the value of X should be increased to lower the similarity judgment standard and reduce false blocking. When spam SMS messages are missed, it indicates that the threshold is set too high, and the value of X should be decreased to increase the similarity judgment standard and reduce missed blocking. Preferably, the initial threshold X can be set to 0.7. After 1-2 weeks of trial operation, it can be fine-tuned based on actual interception data to ensure optimal interception accuracy.

[0033] This invention achieves high efficiency and accuracy in spam SMS interception, breaking through the limitations of existing technologies that rely solely on keyword matching. It leverages the inherent difference in content length between spam and legitimate SMS messages to perform initial screening, quickly eliminating most legitimate messages. Furthermore, it introduces the MO number's network access time as an auxiliary criterion for spam SMS identification, further improving interception efficiency while reducing the probability of false blocking of legitimate messages, thus addressing the issue of incomplete number identification in existing technologies. By calculating the content similarity between the SMS to be intercepted and historical spam SMS messages, it achieves accurate identification of spam SMS messages, improving interception precision.

[0034] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A highly efficient and accurate method for intercepting spam text messages, characterized in that: Includes the following steps: (1) Synchronization of blacklisted numbers: The SMS center synchronizes the list of mobile phone numbers that have been registered in the past year from the operator's customer relationship management (CRM) system at a fixed time every day, and uses this list as the blacklisted numbers; (2) Blacklist storage: The blacklist numbers are inserted into the database of the computer server according to the preset data format to establish a blacklist index for real-time query; (3) Preliminary screening of SMS length: The SMS center calculates and judges the length of each SMS to be sent. If the length of the SMS content is less than or equal to 40 bytes, the SMS is identified as a normal SMS and sent immediately; if the length of the SMS content is greater than 40 bytes, the next step is taken. (4) Sending number verification: Extract the MO (Mobile Originator) of the SMS to be sent, i.e., the sender's mobile phone number, and query the database index to see if the MO number exists in the blacklist; If the query result is not found, the text message will be sent immediately. If the query result indicates that the query exists, proceed to the next step. (5) SMS content similarity matching: The content of the SMS to be sent is compared with the content of each historical spam SMS in the spam SMS content library. If the similarity between a historical spam SMS and the SMS to be sent is greater than the preset threshold X, the SMS to be sent is identified as spam, intercepted and prevented from being sent, and the SMS to be sent is added to the spam SMS content library. If, after matching with all historical spam messages in the spam message content database, the similarity score is not greater than the threshold X, then the message is considered a normal message and is sent normally. (6) Similarity calculation method: The similarity S between the two text messages is calculated using the following formula: S=|S1∩S2| / |S1∪S2|; where S1 is the set of each Chinese character as an element in the first text message, and S2 is the set of each Chinese character as an element in the second text message; S1∩S2 is the intersection of S1 and S2, and |S1∩S2| is the number of elements in the intersection S1∩S2; S1∪S2 is the union of S1 and S2, and |S1∪S2| is the number of elements in the union S1∪S2; (7) Threshold adjustment: The threshold X ranges from 0.5 to 0.9 and is dynamically adjusted according to the actual application scenario of false blocking and missed blocking. When a normal SMS is falsely blocked, the value of X is increased; when spam SMS is missed, the value of X is decreased.

2. The efficient and accurate spam SMS interception method according to claim 1, characterized in that, The fixed time mentioned in step (1) is 00:00 every day. The synchronization operation is completed after establishing a communication connection with the operator's CRM system through a preset interface, which is an HTTP interface.

3. The efficient and accurate spam SMS interception method according to claim 1, characterized in that, The database mentioned in step (2) is a high-performance relational database, including but not limited to MySQL and Oracle. The database supports fast queries with a query response time of no more than 10ms, and the database is set to have a regular automatic backup mechanism.

4. The efficient and accurate spam SMS interception method according to claim 1, characterized in that, The spam SMS content database mentioned in step (5) consists of historically intercepted spam SMS messages and spam SMS messages reported by users. The spam SMS content database is updated regularly to ensure the timeliness and comprehensiveness of the samples.

5. The efficient and accurate spam SMS interception method according to claim 1, characterized in that, The initial value of the threshold X mentioned in step (7) is set to 0.

7. After 1-2 weeks of trial operation, it is fine-tuned according to the actual interception data to achieve the optimal interception accuracy.

6. A highly efficient and accurate spam SMS interception device, characterized in that, The method includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements all the steps of the spam SMS blocking method according to any one of claims 1-5.

7. The highly efficient and accurate spam SMS interception device according to claim 6, characterized in that, It also includes a communication interface and a backup module.

8. A highly efficient and accurate spam SMS interception system, characterized in that, It includes an SMS receiving module, a blacklist synchronization module, a database storage module, a length filtering module, a number verification module, a similarity matching module, an SMS delivery / interception module, a threshold adjustment module, a spam SMS content library, and a CRM system interface. All modules are connected and work together through a communication bus. The SMS receiving module is used to receive all SMS messages to be sent in the mobile communication network and send the SMS content, MO number, and receiving time information to the length filtering module. The CRM system interface is used to establish a communication connection between the blacklist synchronization module and the operator's CRM system, providing support for the synchronization of the mobile phone number list; The blacklist synchronization module is used to synchronize a list of mobile phone numbers that have joined the network in the past year through the CRM system interface at 00:00 every day, generate a blacklist, and send the blacklist to the database storage module. The database storage module is used to store relevant data of blacklisted mobile phone numbers and spam SMS content database, establish data indexes, and provide real-time query support for number verification module and similarity matching module; The length filtering module is used to receive SMS information sent by the SMS receiving module, calculate the length of the SMS content, and if the length is ≤40 bytes, send a sending instruction to the SMS sending / interception module; if the length is >40 bytes, send the SMS information to the number verification module. The number verification module is used to extract the MO number from the SMS message, query the blacklist in the database storage module, and if the number is not in the blacklist, send a delivery instruction to the SMS delivery / interception module; if the number is in the blacklist, send the SMS message to the similarity matching module. The similarity matching module is used to extract the SMS content from the SMS information, perform similarity calculation with the spam SMS content library in the database storage module, and send the calculation result to the SMS delivery / interception module. The SMS delivery / interception module is used to receive instructions from the length filtering module, the number verification module, and the similarity matching module, perform delivery operations on normal SMS messages, perform interception operations on spam SMS messages, and add the intercepted spam SMS messages to the spam SMS content library. The threshold adjustment module is used to dynamically adjust the similarity threshold X based on the interception data of the SMS delivery / interception module, including the number of false blocks and the number of missed blocks, and send the adjusted threshold to the similarity matching module. The spam SMS content database is used to store historically blocked spam SMS messages and spam SMS messages reported by users, providing sample support for the similarity matching module and updating the sample data regularly.

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