CN111984860A - Event context association processing method and system for time sequence data - Google Patents
Event context association processing method and system for time sequence data Download PDFInfo
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Abstract
The invention discloses an event context association processing method and system of time sequence data, firstly defining event context association logic, including two parts of adjacent event association logic and context merging processing logic, after obtaining event data and time stamp, reading intermediate result in computer memory and merging processing, finally carrying out event context association processing to obtain unique context association processing result. The method overcomes the defects of long time consumption and low efficiency in the prior art, and can quickly perform event context correlation processing on massive time sequence data; in addition, the invention calculates the intermediate result in advance, so a large amount of useless repeated operation of the computer is avoided during query, and the response speed is extremely high; in addition, the intermediate result is changed continuously along with the system time movement, and the purpose of smooth movement of the time window can be achieved. The invention can obviously improve the efficiency of event context correlation processing of the time sequence data.
Description
Technical Field
The invention relates to the technical field of data processing systems or methods, in particular to an event context association processing method and system for time sequence data.
Background
Nowadays, internet technology is rapidly developed, and a large number of users perform operations such as page browsing, resource downloading, information searching and the like on the internet. These human operations involve many specific complex events such as staying on a page for 30 seconds, clicking on a link 5 times in a row, uploading data to a website in the size of 2MB, etc. When a plurality of events occur in succession, the context correlation information is included between adjacent events, which can reflect the behavior characteristics of the user activity, or can be used for monitoring some abnormal conditions, such as suddenly increased network traffic, suddenly changed location information, and the like. Therefore, the event context correlation information has high application value, and event context correlation processing is required in the fields of automatic recommendation, data mining, network security, risk monitoring and the like.
However, in a stream processing system, each piece of data input to the system is often processed immediately, and the state of each piece of data is not preserved, so how to maintain the context association information of the data and support fast query is a difficult point. In addition, the current network events have wide sources, large quantity and long spanning time, and the data dimension needing to be analyzed in the event context correlation processing is high and consumes much resources. In such a scenario, the existing event context association processing method and system for time series data have the defects of long time consumption and low efficiency. This is because the processing and query analysis logic of the prior methods and systems is complex and inefficient, involving a large number of unnecessary iterations of computer operations.
Taking the scenario shown in fig. 2 as an example, the prior art will perform the following steps to perform event context association processing: if the event context processing of 16:00-18:00 needs to be carried out, traversing all 4 data in the time period from far to near, processing each pair of adjacent events by using predefined adjacent event association logic, and merging by using predefined context merging processing logic; thereafter, if 15:00-18:00 event context processing is required, then all 8 events need to be traversed. However, it is obvious that there are duplicate logic already executed in the previous process, which can still be executed in the prior art, resulting in unnecessary waste of computer resources and inefficient processing.
Currently, there is a need for a method and a system for processing event context association of time series data, which can overcome the defects of long time consumption and low efficiency in the prior art, quickly perform event context association processing of massive time series data, and support query of a specified time window.
Disclosure of Invention
The invention aims to provide a method and a system for processing event context of time series data aiming at the defects of the prior art.
The purpose of the invention is realized by the following technical scheme: a method for processing event context association of time sequence data is realized by a computer memory, and specifically comprises the following steps:
step 2, when a user operates an induced event, executing an acquisition action on the data and the time stamp of the event; this event is referred to as "this event"; the event with the largest time stamp among events occurring before the current event is referred to as the last event. The acquiring action realizes real-time streaming acquisition by providing a program interface;
step 4, reading an intermediate result R3 in the computer memory in the time period according to the query time period, and performing event context correlation processing; when the query time period spans a plurality of intermediate results R3, all the intermediate results R3 are subjected to intermediate result merging processing one by one from far to near in time; and obtaining a context association processing result according to the unique intermediate result R3 after the intermediate result merging processing.
Further, in step 3, the method for constructing the intermediate result by using the data and the timestamp of the current event includes:
for this event E, there is data d and a timestamp s. Initializing an intermediate result R, wherein the intermediate result has four parts of starting data b, ending data e, an associated processing result R and a time mark t. Setting the values of b and e as d, t as s and r as null. Then R is the intermediate result R2 for the completion of the construction.
Further, the method for merging the intermediate results in step 3 comprises:
the two intermediate results R1 and R2 are sorted by timestamp t, with the timestamp of R1 being less than the timestamp of R2, with R1 preceding R2. The adjacent event association logic defined in step 1 is executed using the termination data e of R1 and the start data b of R2, resulting in an association processing result R0. The context merging processing logic defined in the step 1 is executed by using the association processing results R1 and R2 contained in R0 and R1 and the association processing result R2 contained in R2, and R3 in the new intermediate result R3 is obtained. The value of e for R3 is the value of e for R2; the value of b for R3 is the value of b for R1; the value of t for R3 is the value of t for R1. R3 is the intermediate result merging processing result.
Further, the method for merging the intermediate results in the step 4 comprises the following steps:
assigning the intermediate result R3 which is farthest in time to R1, assigning the intermediate result R3 which is second farthest in time to R2, deleting the intermediate result R3 which is farthest in time and the intermediate result R3 which is second farthest in time, and then merging the R1 and the R2 according to the merging method of the intermediate results in the step 3; the merging process of the intermediate results in the step 4 is continued in this way until a unique intermediate result R3 is obtained, and the context association process result is obtained according to the R value of R3.
Further, the query operation in step 4 and the data and time stamp obtaining action in step 2 can be performed simultaneously.
An event context association processing system of time sequence data, which is realized by a computer memory and comprises an event context association logic definition module, a local event information acquisition module, an event context merging module and an event context association query module:
the event context association logic definition module is used for defining event context association logic aiming at events caused by user operation; the event context association logic comprises two parts, namely adjacent event association logic and context merging processing logic;
the local event information acquisition module is used for executing acquisition behaviors on data and a time stamp of an event when the event is caused by user operation; this event is referred to as "this event"; the event with the largest time stamp among events occurring before the current event is referred to as the last event. The acquiring action realizes real-time streaming acquisition by providing a program interface;
the event context merging module is used for reading an intermediate result R1 in the computer memory according to the timestamp of the current event, and if the current event is the first event, the value of R1 is null; otherwise, the value of R1 is R3 written to computer memory the last time the event context merge module was run; using the data and the timestamp of the current event to construct an intermediate result to obtain R2; processing the R1 and the R2 according to adjacent event association logic to obtain an association processing result, then merging the obtained association processing result and intermediate results of the R1 and the R2 according to context merging processing logic to obtain R3, and writing the R3 into a computer memory;
the event context correlation query module is used for reading an intermediate result R3 in the computer memory in the query time period according to the query time period and performing event context correlation processing; when the query time period spans a plurality of intermediate results R3, all the intermediate results R3 are subjected to intermediate result merging processing one by one from far to near in time; and obtaining a context association processing result according to the unique intermediate result R3 after the intermediate result merging processing.
Further, the method for constructing the intermediate result by using the data and the timestamp of the current event in the event context merging module comprises the following steps:
for this event E, there is data d and a timestamp s. Initializing an intermediate result R, wherein the intermediate result has four parts of starting data b, ending data e, an associated processing result R and a time mark t. Setting the values of b and e as d, t as s and r as null. Then R is the intermediate result R2 for the completion of the construction.
Further, the method for merging and processing the intermediate result in the event context merging module comprises the following steps:
the two intermediate results R1 and R2 are sorted by timestamp t, with the timestamp of R1 being less than the timestamp of R2, with R1 preceding R2. The adjacent event association logic defined in the event context association logic definition module is executed using the termination data e of R1 and the start data b of R2, resulting in an association processing result R0. And executing the context merging processing logic defined in the event context association logic definition module by using the association processing results R1 contained in R0 and R1 and the association processing result R2 contained in R2 to obtain R3 in the new intermediate result R3. The value of e for R3 is the value of e for R2; the value of b for R3 is the value of b for R1; the value of t for R3 is the value of t for R1. R3 is the intermediate result merging processing result.
Further, the method for merging and processing the intermediate results in the event context association query module comprises the following steps:
assigning the intermediate result R3 farthest in time to R1, assigning the intermediate result R3 farthest in time to R2, deleting the intermediate result R3 farthest in time and the intermediate result R3 farthest in time, and merging R1 and R2 according to the merging processing method of the intermediate results in the event context merging module; and continuing the merging processing of the intermediate results in the event context correlation query module in this way until a unique intermediate result R3 is obtained, and obtaining a context correlation processing result according to the R value of R3.
Further, the query module can operate simultaneously with the local event information acquisition module.
The beneficial technical effects of the invention are as follows: firstly, the method overcomes the defects of long time consumption and low efficiency in the prior art, and can quickly perform event context association processing on massive time sequence data; in addition, the invention calculates the intermediate result in advance, so a large amount of useless repeated operation of the computer is avoided during query, and the response speed is extremely high; in addition, the intermediate result is changed continuously along with the system time movement, and the purpose of smooth movement of the time window can be achieved. The invention is suitable for the technical field of event context association processing methods and systems related to time sequence data, and can remarkably improve the efficiency of event context association processing of the time sequence data.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a diagram of a page view event, according to one embodiment;
FIG. 3 is a graph comparing the effect of the present invention and the conventional method.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, embodiments accompanied with figures are described in detail below.
Example 1
Suppose that a website needs to perform event context correlation processing on user behaviors, so as to distinguish traffic caused by normal human users and traffic caused by abnormal machine crawlers. Meanwhile, the website needs to judge whether the machine crawler flow is in a specified time period or not so as to analyze the activity rule of a crawler attacker. The website comprises a plurality of pages, the content of each page is different, the content is different in length, when a normal human user browses the webpage, the HTTP connection maintenance time established between the browser and the website is different due to different content lengths, and the difference between different pages can reach the second level or the minute level. But for web crawlers, the executed program logic is fixed, the text processing speed is very fast, and the different page content lengths do not cause significant HTTP connection maintaining time difference. And in a specified time period, when the minimum value of the adjacent page maintaining time difference is less than 5 seconds, the crawler behavior can be judged. Existing page view records from the same operation source are shown in fig. 2, where the number represents the HTTP connection maintenance time in seconds between the operation source and the page.
The method and the system for processing context association of the time sequence data events are used for judging and inquiring the crawler in a specified time period, and the specific processing steps are as follows:
(1) in the event context association logic definition module, event context association logic is defined. The event context association logic comprises two parts, namely adjacent event association logic and context merging processing logic. Here, the adjacent event correlation logic is defined as "absolute value of current event elapsed time minus last event elapsed time"; context merge processing logic is defined as "minimum".
(2) In the local event information acquisition module, a program interface is provided for acquiring the data and the time stamp of the event in real time. In the embodiment, a POST compiling method is adopted, and the POST is reported by the page js script. In the present embodiment, nine information acquisitions are performed in total.
(3) Reading an intermediate result R1 in a computer memory in an event context merging module according to the time stamp of the current event; using the data and the timestamp of the current event to construct an intermediate result to obtain R2; merging the intermediate results of R1 and R2 to obtain R3, and writing R3 into the memory of the computer.
In the present embodiment, there are nine events, and the intermediate result R1, the R2 constructed from the intermediate result, and the R3 obtained from the merging processing of the intermediate result, which are read from the computer memory each time according to the event timestamp, are shown in the following table:
| time of day | Data of | R1 | R2 | R3 |
| 14:30 | 12 | null | b＝12,e＝12,r＝null,t＝14 | b＝12,e＝12,r＝null,t＝14 |
| 15:10 | 10 | null | b＝10,e＝10,r＝null,t＝15 | b＝10,e＝10,r＝null,t＝15 |
| 15:20 | 40 | b＝10,e＝10,r＝null,t＝15 | b＝40,e＝40,r＝null,t＝15 | b＝10,e＝40,r＝30,t＝15 |
| 15:45 | 80 | b＝10,e＝40,r＝30,t＝15 | b＝80,e＝80,r＝null,t＝15 | b＝10,e＝80,r＝30,t＝15 |
| 15:50 | 8 | b＝10,e＝80,r＝30,t＝15 | b＝8,e＝8,r＝null,t＝15 | b＝10,e＝8,r＝30,t＝15 |
| 16:15 | 12 | null | b＝12,e＝12,r＝null,t＝16 | b＝12,e＝12,r＝null,t＝16 |
| 16:45 | 110 | b＝12,e＝12,r＝null,t＝16 | b＝110,e＝110,r＝null,t＝16 | b＝12,e＝110,r＝98,t＝16 |
| 17:15 | 15 | null | b＝15,e＝15,r＝null,t＝17 | b＝15,e＝15,r＝null,t＝17 |
| 17:45 | 16 | b＝15,e＝15,r＝null,t＝17 | b＝16,e＝16,r＝null,t＝17 | b＝15,e＝16,r＝1,t＝17 |
(4) In the event context correlation query module, reading an intermediate result in a computer memory according to a query time period, and performing event context correlation processing; when the query time period spans a plurality of intermediate results, merging all the intermediate results one by one from far to near according to time; the query module may operate concurrently with the local event information acquisition module.
In this embodiment, the crawler crawling behaviors from 15:00 to 18:00 are queried, the intermediate results on each timestamp are read first, and then are merged one by one from far to near, that is, the intermediate results from 15:00 to 16:00 and from 16:00 to 17:00 are read, then are merged, and then are merged with the intermediate results from 17:00 to 18:00, so that the event context association processing result can be obtained. The specific process is shown in the following table:
according to the result, r is 1 and is less than 5 seconds, and the crawler behavior characteristics are met, so that the method carries out event context processing, and successfully detects the crawler behavior according to the specified query time period of 15:00-18: 00.
Example 2
To demonstrate the practical benefit of the method of the present invention, we performed a set of experiments. Compared with the traditional method, the method disclosed by the invention has the advantage that the effect of the method disclosed by the invention is shown in a shorter time than that of the traditional method when the same data amount is processed.
The experimental scene is as follows: random 5 ten thousand sets of test data are generated, each set of test data including a time stamp and an event data. The timestamps represent the timing of the data, and the event data represents the data needed in the defined event context correlation logic. The test data were distributed over 50 consecutive hours with 1000 groups per hour. The time stamps of each group of data are uniformly distributed in the hour, and the event data are randomly generated.
The test method comprises the following steps: the method and the traditional method are respectively used for processing and inquiring the context association processing results of every 1 or 2.. 50 hours of events, then the time of each test is recorded, and finally a chart is drawn.
The test results are shown in fig. 3. From the results it can be seen that:
(1) the processing time of the traditional method is far longer than that of the method of the invention.
(2) As the amount of data grows, the processing time of the conventional method grows linearly, while the method of the present invention remains substantially unchanged.
The above-described embodiments are intended to illustrate rather than to limit the invention, and any modifications and variations of the present invention are within the spirit of the invention and the scope of the appended claims.
Claims (10)
1. A method for processing event context association of time sequence data is realized by a computer memory, and specifically comprises the following steps:
step 1, defining event context association logic aiming at an event caused by user operation; the event context association logic comprises two parts, namely adjacent event association logic and context merging processing logic;
step 2, when a user operates an induced event, executing an acquisition action on the data and the time stamp of the event; this event is referred to as "this event"; the event with the largest time stamp among events occurring before the current event is referred to as the last event. The acquiring action realizes real-time streaming acquisition by providing a program interface;
step 3, reading an intermediate result R1 in the computer memory according to the timestamp of the current event, wherein if the current event is a first event, the value of R1 is null; otherwise, the value of R1 is R3 written to computer memory at the last event performed step 3; using the data and the timestamp of the current event to construct an intermediate result to obtain R2; processing the R1 and the R2 according to adjacent event association logic to obtain an association processing result, then merging the obtained association processing result and intermediate results of the R1 and the R2 according to context merging processing logic to obtain R3, and writing the R3 into a computer memory;
step 4, reading an intermediate result R3 in the computer memory in the time period according to the query time period, and performing event context correlation processing; when the query time period spans a plurality of intermediate results R3, all the intermediate results R3 are subjected to intermediate result merging processing one by one from far to near in time; and obtaining a context association processing result according to the unique intermediate result R3 after the intermediate result merging processing.
2. The event context association processing method of time series data according to claim 1, wherein the method for constructing the intermediate result using the data and the timestamp of the current event in step 3 comprises:
for this event E, there is data d and a timestamp s. Initializing an intermediate result R, wherein the intermediate result has four parts of starting data b, ending data e, an associated processing result R and a time mark t. Setting the values of b and e as d, t as s and r as null. Then R is the intermediate result R2 for the completion of the construction.
3. The method for processing event context association of time series data according to claim 1, wherein the method for merging the intermediate results in step 3 is:
the two intermediate results R1 and R2 are sorted by timestamp t, with the timestamp of R1 being less than the timestamp of R2, with R1 preceding R2. The adjacent event association logic defined in step 1 is executed using the termination data e of R1 and the start data b of R2, resulting in an association processing result R0. The context merging processing logic defined in the step 1 is executed by using the association processing results R1 and R2 contained in R0 and R1 and the association processing result R2 contained in R2, and R3 in the new intermediate result R3 is obtained. The value of e for R3 is the value of e for R2; the value of b for R3 is the value of b for R1; the value of t for R3 is the value of t for R1. R3 is the intermediate result merging processing result.
4. The method for processing event context association of time series data according to claim 1, wherein the method for merging the intermediate results in step 4 comprises:
assigning the intermediate result R3 which is farthest in time to R1, assigning the intermediate result R3 which is second farthest in time to R2, deleting the intermediate result R3 which is farthest in time and the intermediate result R3 which is second farthest in time, and then merging the R1 and the R2 according to the merging method of the intermediate results in the step 3; the merging process of the intermediate results in the step 4 is continued in this way until a unique intermediate result R3 is obtained, and the context association process result is obtained according to the R value of R3.
5. The method for processing event context of time series data according to claim 1, wherein the query operation in step 4 and the data and timestamp obtaining in step 2 can be performed simultaneously.
6. An event context association processing system of time series data is characterized in that the system is realized by a computer memory and comprises an event context association logic definition module, a local event information acquisition module, an event context merging module and an event context association query module:
the event context association logic definition module is used for defining event context association logic aiming at events caused by user operation; the event context association logic comprises two parts, namely adjacent event association logic and context merging processing logic;
the local event information acquisition module is used for executing acquisition behaviors on data and a time stamp of an event when the event is caused by user operation; this event is referred to as "this event"; the event with the largest time stamp among events occurring before the current event is referred to as the last event. The acquiring action realizes real-time streaming acquisition by providing a program interface;
the event context merging module is used for reading an intermediate result R1 in the computer memory according to the timestamp of the current event, and if the current event is the first event, the value of R1 is null; otherwise, the value of R1 is R3 written to computer memory the last time the event context merge module was run; using the data and the timestamp of the current event to construct an intermediate result to obtain R2; processing the R1 and the R2 according to adjacent event association logic to obtain an association processing result, then merging the obtained association processing result and intermediate results of the R1 and the R2 according to context merging processing logic to obtain R3, and writing the R3 into a computer memory;
the event context correlation query module is used for reading an intermediate result R3 in the computer memory in the query time period according to the query time period and performing event context correlation processing; when the query time period spans a plurality of intermediate results R3, all the intermediate results R3 are subjected to intermediate result merging processing one by one from far to near in time; and obtaining a context association processing result according to the unique intermediate result R3 after the intermediate result merging processing.
7. The event context correlation processing system for time series data according to claim 6, wherein the method for constructing the intermediate result by using the data and the timestamp of the current event in the event context merging module comprises:
for this event E, there is data d and a timestamp s. Initializing an intermediate result R, wherein the intermediate result has four parts of starting data b, ending data e, an associated processing result R and a time mark t. Setting the values of b and e as d, t as s and r as null. Then R is the intermediate result R2 for the completion of the construction.
8. The system for event context correlation processing of time series data according to claim 6, wherein the method for merging and processing the intermediate result in the event context merging module is:
the two intermediate results R1 and R2 are sorted by timestamp t, with the timestamp of R1 being less than the timestamp of R2, with R1 preceding R2. The adjacent event association logic defined in the event context association logic definition module is executed using the termination data e of R1 and the start data b of R2, resulting in an association processing result R0. And executing the context merging processing logic defined in the event context association logic definition module by using the association processing results R1 contained in R0 and R1 and the association processing result R2 contained in R2 to obtain R3 in the new intermediate result R3. The value of e for R3 is the value of e for R2; the value of b for R3 is the value of b for R1; the value of t for R3 is the value of t for R1. R3 is the intermediate result merging processing result.
9. The event context correlation processing system of time series data according to claim 6, wherein the method for merging and processing the intermediate results in the event context correlation query module is:
assigning the intermediate result R3 farthest in time to R1, assigning the intermediate result R3 farthest in time to R2, deleting the intermediate result R3 farthest in time and the intermediate result R3 farthest in time, and merging R1 and R2 according to the merging processing method of the intermediate results in the event context merging module; and continuing the merging processing of the intermediate results in the event context correlation query module in this way until a unique intermediate result R3 is obtained, and obtaining a context correlation processing result according to the R value of R3.
10. The system of claim 6, wherein the query module is capable of running concurrently with the local event information acquisition module.
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