CN107864050A - Server failure Effective Association Rules analysis method based on lattice structure - Google Patents

Server failure Effective Association Rules analysis method based on lattice structure Download PDF

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Publication number
CN107864050A
CN107864050A CN201710983382.8A CN201710983382A CN107864050A CN 107864050 A CN107864050 A CN 107864050A CN 201710983382 A CN201710983382 A CN 201710983382A CN 107864050 A CN107864050 A CN 107864050A
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item
effectiveness
association rules
item collection
collection
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彭云竹
胡洛娜
石林鑫
厉仄平
陈秋地
李果
杨硕
刘尚
郝海泉
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State Grid Corp of China SGCC
Information and Telecommunication Branch of State Grid Chongqing Electric Power Co Ltd
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State Grid Corp of China SGCC
Information and Telecommunication Branch of State Grid Chongqing Electric Power Co Ltd
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Publication of CN107864050A publication Critical patent/CN107864050A/en
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/06—Management of faults, events, alarms or notifications
    • H04L41/0631—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
    • G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
    • G06F11/0766—Error or fault reporting or storing
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/06—Management of faults, events, alarms or notifications
    • H04L41/069—Management of faults, events, alarms or notifications using logs of notifications; Post-processing of notifications
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/147—Network analysis or design for predicting network behaviour
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
    • H04L43/0805—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability
    • H04L43/0817—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability by checking functioning
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/16—Threshold monitoring

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Theoretical Computer Science (AREA)
  • Quality & Reliability (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Environmental & Geological Engineering (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

A kind of server failure Effective Association Rules analysis method based on lattice structure provided by the invention, extract the log information in server system event log storehouse, fault log data storehouse is formed, using lattice structure, each item collection in item collection lattice is made up of following three attribute：Item collection X, support s, effectiveness u (X), deletion is poorly efficient to use item collection, generates effective item collection lattice HUIL, utilizes HUIL to carry out the efficient rule association of server failure and analyzes；Enable to established correlation rule that there is stronger failure prediction capability, so as to realize the accurate detection to server failure and analysis, provided safeguard for the continual and steady operation of server, and whole method is simple, effectively improves the efficiency of whole analysis process.

Description

Server failure Effective Association Rules analysis method based on lattice structure
Technical field
The present invention relates to a kind of failure analysis methods, more particularly to a kind of server failure efficient correlation based on lattice structure Rule analysis method.
Background technology
At present, server is widely used in all trades and professions, penetrates into the every aspect of society, and it stores mass data letter Breath, is one of most basic, most important equipment in network.Most industries require server can it is round-the-clock provide safety, Stable, efficient network service, if the failure occurred cannot be properly settled in time, can not just ensure the smooth of network service It is logical, or even can also trigger bigger failure, so the accident analysis of server just seems most important.
Association analysis method is one kind in server failure analysis, assigns the certain failure predication of server system and place Ability is put, a knowledge base is established using the conventional fault log of server, is got according to real-time monitoring server running status Data matched with the information in knowledge base, so as to predict potential failure and risk and respond in time.So The safe operation time of server, efficient offer network service can be provided；In the prior art, for server event The association analysis method of barrier mainly has following several：Apriori algorithm and FP-growth algorithms；Wherein, Apriori algorithm makes With a kind of alternative manner for being referred to as and successively searching for, k- item collections are used to explore (k+1)-item collection.First, the collection of frequent 1- item collections is found out Close.It is denoted as L1, L1For finding out the set L of frequent 2- item collections2, it is used further to find out L3, so on, until that can not find frequently K- item collections；Look for each LkRun-down database is needed, so that processing speed is slow, efficiency is low.FP-growth algorithms By twice sweep transaction database, the frequent item that each office includes is arrived FP- by the compression storage of its support descending In tree.During finding frequent mode afterwards, it is not necessary to scan transaction database again, and carried out only in FP-Tree Lookup, and frequent mode is directly produced by recursive call FP-growth method, in whole discovery procedure also not Candidate pattern need to be produced, therefore, FP-growth algorithms improve efficiency to a certain extent compared to Apriori algorithm, but It is that its efficiency is still very low, this is due to that the log information of server is complicated, and there is substantial amounts of redundancy, and FP-growth algorithms effectively can not carry out rationalization processing to redundancy, so that final efficiency is low, application Effect is poor, and its failure prediction capability is poor.
It is, therefore, desirable to provide a kind of new server failure Association Rule Analysis method, enables to established association Rule has stronger failure prediction capability, so as to realize the accurate detection to server failure and analysis, for holding for server Continuous stable operation provides safeguard, and whole method is simple, effectively improves the efficiency of whole analysis process.
The content of the invention
In view of this, it is an object of the invention to provide a kind of server failure Effective Association Rules analysis based on lattice structure Method, enable to established correlation rule that there is stronger failure prediction capability, so as to realize the essence to server failure Really detection and analysis, provide safeguard for the continual and steady operation of server, and whole method is simple, effectively improves whole analysis The efficiency of process.
A kind of server failure Effective Association Rules analysis method based on lattice structure provided by the invention, including following step Suddenly：
S2. the log information in the fault log data storehouse of server is pre-processed to obtain the set i=of data item {i1,i2,…,im, wherein, m is the number of data item；
S3. setting time T is divided into n period, in d-th of period TdThe data item i of interior event of failurep(q (ip,td)) one affairs t of compositiond, and d by n transaction sets into affairs set D={ t1,t2,…,td,…,tn, wherein, 1≤d ≤ n, q (ip,td) it is in period TdThe data item i of internal fault eventpThe number of appearance, ipFor the set i={ i of data item1, i2,…,imIn pth item, 1≤p≤m；
S4. all item collections occurred in the set of data item are enumerated using lattice structure, each item collection in item collection lattice It is made up of following three attribute：Item collection X, support s and effectiveness u (X)；
S5. by the effectiveness u (X) of item collection and the minimum value of utility u of settingminIt is compared, deletes effectiveness and be less than minimum effectiveness The item collection of value, remaining item collection is formed into effective item collection lattice HUIL；
S6., Effective Association Rules set Rulesset is set, and is by efficient regular collection Rulesset initializing sets Empty set, and the minimum effectiveness confidence value of Effective Association Rules is set as min-uconf；
S7. all 1- item collections searched in effective item collection lattice HUIL, and the 1- item collections of no child node are deleted, and obtain 1- item collection set H；
S8. the Effective Association Rules set for guide service device accident analysis is determined according to 1- item collection set H Ruleset。
Further, comprise the following steps in step S8：
S801. to the data item i of the event of failure in any 1- item collections set Hp, searched in effective item collection lattice HUIL Rope includes data item ipAll item collections, and effective item collection lattice HUIL is included into data item ipAll item collections composition item collection collection Close H*；
S802. according to effective item collection lattice HUIL lattice structure direction top down, item collection set H is set*In have son One of k- item collections of node are Xk, item collection set H is set*In one of them (k+j)-item collection be Xk, whereinIt is X so as to obtain candidate rule Rk.Itemset →Xk+j.Itemset\Xk.Itesmset, candidate is calculated The R of rule effectiveness confidence level uconf (R)；
S803. such as effectiveness confidence level uconf (R) >=min-uconf of current candidate rule, then an efficient correlation is obtained Regular R, and Effective Association Rules R is added to Effective Association Rules set Rulesset；
S804. item collection set H is traveled through according to step 801 to step 803*In all item collections, generation Effective Association Rules simultaneously It is added in Effective Association Rules set Ruleset.
S805. step S801 to step S804 is performed to remaining all 1- item collections in 1- item collection set H, obtained final Effective Association Rules set Ruleset for guide service device accident analysis.
Further, in step S4, item collection X effectiveness u (X) is calculated according to equation below：
Wherein, u (X, td) for item collection X in affairs tdIn Effectiveness.
Further, effectiveness u (X, t are calculated according to equation belowd)：
Wherein, u (ip,td) be any event of failure data item ipIn thing Be engaged in tdIn effectiveness.
Further, effectiveness u (i are calculated according to equation belowp,td)：
u(ip,td)=p (ip)×q(ip,td), wherein, p (ip) be event of failure data item ipImportant level value.
Further, effectiveness confidence level uconf (R) is calculated according to equation below in step S802：
Wherein, luv (xk,Xk+j) it is k- item collections XkIn k+j- item collections Xk+jLocal effectiveness, u (Xk) it is k- item collections XkEffectiveness.
Further, local effectiveness luv (x are calculated according to equation belowk,Xk+j)：
Wherein, luv (xi,Xk) it is k- Item collection XkAny one of xkLocal effectiveness.
Further, k- item collections X is calculated according to equation belowkAny one of xkLocal effectiveness be luv (xi,Xk)：
Further, in addition to step S1：Pre-processed as follows：
Log information is extracted from server event daily record storehouse and forms fault log data storehouse；
Log information in fault log data storehouse is mapped to the set i={ i to form data item1,i2,…,im, and press According to data item significance level to data item ipSetting important level value p (ip)。
Further, log information is mapped as by data item according to following method：
The attribute of log information is represented using English alphabet, the value of log information is represented with Arabic numerals.
Beneficial effects of the present invention：By means of the invention it is possible to so that the correlation rule established has stronger failure pre- Survey ability, so as to realize the accurate detection to server failure and analysis, provided safeguard for the continual and steady operation of server, and And entirely method is simple, the efficiency of whole analysis process is effectively improved.
Embodiment
The present invention is further described in detail below：
A kind of server failure Effective Association Rules analysis method based on lattice structure provided by the invention, including following step Suddenly：
S1：Pre-processed as follows：
Log information is extracted from server event daily record storehouse and forms fault log data storehouse；By in fault log data storehouse Log information map the set i={ i to form data item1,i2,…,im, and according to data item significance level to data item ipSetting important level value p (ip), log information is mapped as by data item, 1≤p≤m according to following method；
The attribute of log information is represented using English alphabet, the value of log information is represented with Arabic numerals.
S2. the log information in the fault log data storehouse of server is pre-processed to obtain the set i=of data item {i1,i2,…,im, wherein, m is the number of data item；
S3. setting time T is divided into n period, in d-th of period TdThe data item i of interior event of failurep(q (ip,td)) one affairs t of compositiond, and d by n transaction sets into affairs set D={ t1,t2,…,td,…,tn, wherein, 1≤d ≤ n, q (ip,td) it is in period TdThe data item i of internal fault eventpThe number of appearance, ipFor the set i={ i of data item1, i2,…,imIn pth item, 1≤p≤m；
S4. all item collections occurred in the set of data item are enumerated using lattice structure, each item collection in item collection lattice It is made up of following three attribute：Item collection X, support s and effectiveness u (X)；
S5. by the effectiveness u (X) of item collection compared with the minimum value of utility umin set, effectiveness is deleted less than most poorly efficient With the item collection of value, remaining item collection is formed into effective item collection lattice HUIL；
S6., Effective Association Rules set Rulesset is set, and is by efficient regular collection Rulesset initializing sets Empty set, and the minimum effectiveness confidence value of Effective Association Rules is set as min-uconf；
S7. all 1- item collections searched in effective item collection lattice HUIL, and the 1- item collections of no child node are deleted, and obtain 1- item collection set H；
S8. the Effective Association Rules set for guide service device accident analysis is determined according to 1- item collection set H Ruleset.By the method for the present invention, the difference of item collection effectiveness is taken into full account in processing procedure, so as to effectively pick Except the influence to processing procedure such as redundancy, enable to established correlation rule that there is stronger failure predication energy Power, so as to realize the accurate detection to server failure and analysis, provided safeguard for the continual and steady operation of server, and it is whole Individual method is simple, effectively improves the efficiency of whole analysis process
In the present embodiment, comprise the following steps in step S8：
S801. to the data item i of the event of failure in any 1- item collections set Hp, searched in effective item collection lattice HUIL Rope includes data item ipAll item collections, and effective item collection lattice HUIL is included into data item ipAll item collections composition item collection collection Close H*；
S802. according to effective item collection lattice HUIL (for the Lattice of High Utility Itemsets, HUIL Abbreviation) lattice structure direction top down, item collection set H is set*In have child node one of k- item collections be XkIf Put item collection set H*In one of them (k+j)-item collection be Xk, whereinIt is so as to obtain candidate rule R Xk.Itemset→Xk+j.Itemset\ Xk.Itesmset, the R of candidate rule effectiveness confidence level uconf (R) is calculated；
S803. such as effectiveness confidence level uconf (R) >=min-uconf of current candidate rule, then an efficient correlation is obtained Regular R, and Effective Association Rules R is added to Effective Association Rules set Rulesset；
S804. item collection set H is traveled through according to step 801 to step 803*In all item collections, generation Effective Association Rules simultaneously It is added in Effective Association Rules set Ruleset.
S805. step S801 to step S804 is performed to remaining all 1- item collections in 1- item collection set H, obtained final For the Effective Association Rules set Ruleset of guide service device accident analysis, by the above method, can select accurately Effective Association Rules, so that last Effective Association Rules have stronger failure prediction capability, and then ensure server The accurate detection of failure and analysis.
In the present embodiment, in step S4, item collection X effectiveness u (X) is calculated according to equation below：
Wherein, u (X, td) for item collection X in affairs tdIn Effectiveness.
Wherein：Effectiveness u (X, t are calculated according to equation belowd)：
Wherein, u (ip,td) be any event of failure data item ipIn thing Be engaged in tdIn effectiveness.
Effectiveness u (i are calculated according to equation belowp,td)：
u(ip,td)=p (ip)×q(ip,td), wherein, p (ip) be event of failure data item ipImportant level value, lead to Cross the above method, can accurately at evaluation item collection value of utility, and then embody the utilization variance of item collection, be follow-up processing Provide safeguard, and improve the efficiency of whole process.
In the present embodiment, effectiveness confidence level uconf (R) is calculated according to equation below in step S802：
Wherein, luv (xk,Xk+j) it is k- item collections XkIn k+j- item collections Xk+jLocal effectiveness, u (Xk) it is k- item collections XkEffectiveness.
Wherein, local effectiveness luv (x are calculated according to equation belowk,Xk+j)：
Wherein, luv (xi,Xk) it is k- Item collection XkAny one of xkLocal effectiveness.
K- item collections X is calculated according to equation belowkAny one of xkLocal effectiveness be luv (xi,Xk)：
By the above method, on the basis of item collection effectiveness, provided safeguard for final accurate determination Effective Association Rules, Beneficial to the efficiency for improving whole processing procedure.
Below by way of an instantiation to the detailed description of the invention：
It is below the log information in the fault log data storehouse of certain server, as illustrated in chart 1：
Table 1
Table 2
Table 2 is to map the log information in table 1 in the log database to be formed according to the mapping method in step S1 Data item, by taking temperature as an example：A represents attribute information, as temperature event, and 0,1 and 2 expression is that the attribute of log information takes Value, three different property values of temperature event are finally represented by A0, A1 and A2, and important level value is carried out according to significance level Setting.
Also, it will averagely be divided into n=9 period, a period T in 2016 4,5, JunedIn (1≤d≤n) All event of failures in item ip(q(ip,td)) one affairs t of compositiond(1≤d≤n), wherein q (ip,td) represent in the time Section TdMean terms ipThe number of appearance, obtain affairs set D={ t1,t2,...,tn}；After the method in above-mentioned, draw as follows Rule：
1. temperature exceeds danger threshold → CPU overtemperature protections, restarted (uconf=86%)；
2. temperature is in fence coverage → CPU overtemperature protections (uconf=61%)；
3. internal memory ECC error → blue screen (uconf=90%)
4. software NMI → machine of delaying (uconf=75%)
5. there is exception → cpu fan high-speed cruising (uconf=63%) in backboard fan
6. battery electric quantity is low → OS starts and closes (uconf=78%)
7. power supply supply power mismatch → case fan rotating speed is low and temperature exceeds secure threshold (uconf= 73%)
8. power supply supply voltage mismatch → CPU voltages are less than secure threshold and machine of delaying (uconf=76%)；Therefore, Pass through above-mentioned method, you can the failure of server is detected and predicted, so that the temperature of server is too high as an example, if Know that temperature exceeds danger threshold, then it may determine that the failure that server is likely to occur either is restarted for CPU overtemperature protections, its Effectiveness confidence level reaches 86%, whereas if Server Restart or generation overtemperature protection, then the failure that can be determined that is temperature Degree did danger threshold, and temperature is in fence coverage in other words, and effectiveness confidence level has respectively reached 86% or 61%, therefore, Pass through the method for the present invention so that there is stronger forecast analysis ability for the failure of server, and there is stronger inspection Analysis ability is surveyed, so as to Accurate Prediction or the failure of server is detected, effectively improves the O＆M efficiency of server, protect Card server can continually and steadily be run, and whole process is more succinct reliable, efficiency high.
Finally illustrate, the above embodiments are merely illustrative of the technical solutions of the present invention and it is unrestricted, although with reference to compared with The present invention is described in detail good embodiment, it will be understood by those within the art that, can be to the skill of the present invention Art scheme is modified or equivalent substitution, and without departing from the objective and scope of technical solution of the present invention, it all should cover at this Among the right of invention.

Claims (10)

  1. A kind of 1. server failure Effective Association Rules analysis method based on lattice structure, it is characterised in that：Comprise the following steps：
    S2. the log information in the fault log data storehouse of server is pre-processed to obtain the set i={ i of data item1, i2,…,im, wherein, m is the number of data item；
    S3. setting time T is divided into n period, in d-th of period TdThe data item i of interior event of failurep(q(ip, td)) one affairs t of compositiond, and d by n transaction sets into affairs set D={ t1,t2,…,td,…,tn, wherein t, d (1≤d≤ N) q (ip,td) it is in period TdThe data item i of internal fault eventpThe number of appearance, ipFor the set i={ i of data item1, i2,…,imIn pth item, 1≤p≤m；
    S4. all item collections occurred in the set of data item are enumerated using lattice structure, each item collection in item collection lattice is by such as Lower three attributes composition：Item collection X, support s and effectiveness u (X)；
    S5. by the effectiveness u (X) of item collection and the minimum value of utility u of settingminIt is compared, deletes effectiveness less than minimum value of utility Item collection, remaining item collection is formed into effective item collection lattice HUIL；
    S6., Effective Association Rules set Rulesset is set, and is empty set by efficient regular collection Rulesset initializing sets, And the minimum effectiveness confidence value of Effective Association Rules is set as min-uconf；
    S7. all 1- item collections searched in effective item collection lattice HUIL, and the 1- item collections of no child node are deleted, and obtain 1- items Collect set H；
    S8. the Effective Association Rules set Ruleset for guide service device accident analysis is determined according to 1- item collection set H.
  2. 2. the server failure Effective Association Rules analysis method based on lattice structure according to claim 1, it is characterised in that： Comprise the following steps in step S8：
    S801. to the data item i of the event of failure in any 1- item collections set Hp, search for and include in effective item collection lattice HUIL Data item ipAll item collections, and effective item collection lattice HUIL is included into data item ipAll item collections composition item collection set H*；
    S802. according to effective item collection lattice HUIL lattice structure direction top down, item collection set H is set*In have child node One of k- item collections are Xk, item collection set H is set*In one of them (k+j)-item collection be Xk, wherein It is X so as to obtain candidate rule Rk.Itemset→Xk+j.Itemset\Xk.Itesmset, the effectiveness for calculating the R of candidate rule is put Reliability uconf (R)；
    S803. such as effectiveness confidence level uconf (R) >=min-uconf of current candidate rule, then an Effective Association Rules are obtained R, and Effective Association Rules R is added to Effective Association Rules set Rulesset；
    S804. item collection set H is traveled through according to step 801 to step 803*In all item collections, generation Effective Association Rules simultaneously be added to In Effective Association Rules set Ruleset.
    S805. step S801 to step S804 is performed to remaining all 1- item collections in 1- item collection set H, obtains final be used for The Effective Association Rules set Ruleset of guide service device accident analysis.
  3. 3. the server failure Effective Association Rules analysis method based on lattice structure according to claim 2, it is characterised in that： In step S4, item collection X effectiveness u (X) is calculated according to equation below：
    Wherein, u (X, td) for item collection X in affairs tdIn effectiveness.
  4. 4. the server failure Effective Association Rules analysis method based on lattice structure according to claim 3, it is characterised in that： Effectiveness u (X, t are calculated according to equation belowd)：
    Wherein, u (ip,td) be any event of failure data item ipIn affairs tdIn Effectiveness.
  5. 5. the server failure Effective Association Rules analysis method based on lattice structure according to claim 4, it is characterised in that： Effectiveness u (i are calculated according to equation belowp,td)：
    u(ip,td)=p (ip)×q(ip,td), wherein, p (ip) be event of failure data item ipImportant level value.
  6. 6. the server failure Effective Association Rules analysis method based on lattice structure according to claim 5, it is characterised in that： Effectiveness confidence level uconf (R) is calculated according to equation below in step S802：
    Wherein, luv (xk,Xk+j) it is k- item collections XkIn k+j- item collections Xk+j's Local effectiveness, u (Xk) it is k- item collections XkEffectiveness.
  7. 7. the server failure Effective Association Rules analysis method based on lattice structure according to claim 6, it is characterised in that： Local effectiveness luv (x are calculated according to equation belowk,Xk+j)：
    Wherein, luv (xi,Xk) it is k- item collections Xk Any one of xkLocal effectiveness.
  8. 8. the server failure Effective Association Rules analysis method based on lattice structure according to claim 7, it is characterised in that： K- item collections X is calculated according to equation belowkAny one of xkLocal effectiveness be luv (xi,Xk)：
    <mrow> <mi>l</mi> <mi>u</mi> <mi>v</mi> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>,</mo> <msub> <mi>X</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <munder> <mo>&amp;Sigma;</mo> <mrow> <msub> <mi>X</mi> <mi>k</mi> </msub> <mo>&amp;SubsetEqual;</mo> <msub> <mi>t</mi> <mi>d</mi> </msub> <mo>&amp;cap;</mo> <msub> <mi>t</mi> <mi>d</mi> </msub> <mo>&amp;Element;</mo> <mi>D</mi> </mrow> </munder> <mi>u</mi> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>,</mo> <msub> <mi>t</mi> <mi>d</mi> </msub> <mo>)</mo> </mrow> <mo>.</mo> </mrow>
  9. 9. the server failure Effective Association Rules analysis method based on lattice structure according to claim 1, it is characterised in that： Also include step S1：Pre-processed as follows：
    Log information is extracted from server event daily record storehouse and forms fault log data storehouse；
    Log information in fault log data storehouse is mapped to the set i={ i to form data item1,i2,…,im, and according to number According to the significance level of item to data item ipSetting important level value p (ip)。
  10. 10. the server failure Effective Association Rules analysis method based on lattice structure, its feature exist according to claim 9 In：Log information is mapped as by data item according to following method：
    The attribute of log information is represented using English alphabet, the value of log information is represented with Arabic numerals.
CN201710983382.8A 2017-10-20 2017-10-20 Server failure Effective Association Rules analysis method based on lattice structure Pending CN107864050A (en)

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