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Expert Systems with Applications 38 (2011) 1709–1715 Contents lists available at ScienceDirect Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswaPreprocessing expert system for mining association rulesin telecommunication networksTong-Yan Li a,⇑, Xing-Ming Li ba Department of Communication Engineering, Chengdu University of Information Technology, Chengdu 610225, Chinab Key Laboratory of Broadband Optical Fiber Transmission and Communication Networks of Ministry of Education, UESTC, Chengdu 610054, Chinaa r t i c l e i n f o a b s t r a c tKeywords: Recently, the application of association rules mining becomes an important research area in alarm corre-Alarm correlation analysis lation analysis. However, the original alarms in the telecommunication networks cannot be used to minePreprocessing expert system association rules directly. This paper proposes a novel preprocessing expert system model to deal withAssociation rules mining the original alarms. This model uses two important techniques, of which the time window techniqueNeural network is used for converting original alarms into transactions, and the neural network technique can classifyWeighted association rules the alarms with different levels according to the characteristics of telecommunication networks in order to mine the weighted association rules. Simulation results and the real-world applications demonstrate the effectiveness and practicality of this preprocessing expert system. Ó 2010 Elsevier Ltd. All rights reserved.1. Introduction An alarm correlation system should be adapted to the fast changing technical advances in the telecommunication domain. It Recent global expansion in the demand for telecommunications is well known that TASA (Telecommunication Alarm Sequenceservices has resulted in a considerable growth of networks in terms Analyzer) (Hatonen et al., 1996a, 1996b; Klemettinen, Mannila, &of size, complexity and bandwidth. Networks often consist of hun- Toivonen, 1999) is a classical knowledge discovery system for ana-dreds or even thousands of interconnected nodes from different lyzing large alarm databases from telecommunication networks.manufacturers using various transport mediums and systems. As TASA supports two central phase of the knowledge discovery pro-a result, when a network problem or failure occurs, it is possible cess: the pattern discovery process and the rules presentationthat a very large volume of alarms are generated. These alarms de- phase. In the ﬁrst process, TASA ﬁnds automatically episode rulesscribe lots of detailed but very fragmented information about the and association rules, and in the rule presentation phase, someproblems. Typically, alarm ﬂow is useful to ﬁnd and isolate faults. powerful pruning, ordering, and grouping tools are used to supportHowever, it is also very difﬁcult to determine the root cause of the large sets of rules. Obviously, the algorithms of TASA in pattern dis-faults. As we know, Alarm correlation is used to be helpful in the covery process are based on the Apriori algorithm (Agrawal &faults diagnosis and localization (Amani, Fathi, & Dehghan, 2005; Srikant, 1994; Ng, Lakshmanan, Han, & Pang, 1998; Sarawagi,Hou & Zhang, 2008; Tang, Al-Shaer, & Boutaba, 2008). In the past, Thomas, & Agrawal, 1998; Srikant, Vu, & Agrawal, 1997), it failsthe knowledge of alarm correlation was mainly obtained by net- to reﬂect some characteristics of alarms effectively. For example,work experts. With the development of telecommunication net- alarms from telecommunication network are always consideredworks, it increasingly difﬁcult for experts to keep up with the inequity, and they are usually made of short messages with generalrapid change of networks and discover the real useful knowledge textual formats. In particular, such massage includes informationfrom alarms. Therefore, researchers adopt many advanced meth- about the creation time of alarm, the observed symptom of faultods including data mining to analyze alarm correlation. Data min- and the device issuing the alarm. Therefore, we consider that theing is a science of extracting implicit, previously unknown, and items should be given different weights to reﬂect their importancepotentially useful information from large data sets or databases, in alarm correlation analysis. On the other hand, the strategy ofalso known as knowledge discovery in databases (KDD). Telecom- ﬁnding frequent items would prune off infrequent items whichmunication alarm correlation analysis based on data mining is now may include some useful relationships of association patterns. Inplaying an important part in current research and drawing more fact, although rare events do not happen often or regularly, they of-and more attentions. ten have special meaning or play an important role in some situa- tion as predicting telecommunication equipment failures. It turns ⇑ Corresponding author. out that the alarm with weight can help ﬁnd the rare but important E-mail address: sunny60138800@yahoo.com.cn (T.-Y. Li). information. In addition, alarms in the telecommunication0957-4174/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved.doi:10.1016/j.eswa.2010.07.096
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1710 T.-Y. Li, X.-M. Li / Expert Systems with Applications 38 (2011) 1709–1715networks are massive, bursting and intermittent. Although many can be extracted to form alarm events for mining weightedmethods (Bouloustas, Calo, & Finkel, 1994; Marilly, Aghasaryan, association rules.Betgé-Brezetz, O’Martinot, & Delègue, 2002; Weiss & Hirsh, 1998) Incomplete data: in some special circumstances, some informa-have been proposed to analyze the alarm correlation, few methods tion may be lost for lack of some alarm data. Both network man-took account of how to deal with the original alarm data. agement channels interrupted and information transmitted In this paper, we propose a novel preprocessing expert system unsuccessfully may lead to this problem.to resolve above problems. In order to ﬁnd out the root cause of Noise in the data: in the mining process, the data which is unre-alarms and locate the faults accurately by using alarm correlation lated to fault diagnosis can be called noise. Noise has greatanalysis, the processing time should be shortened for the need of interference with alarm correlation analysis, for instance, itboth intelligent network management and automation. During must be removed in preprocessing.the process of data preprocessing, the framework of the knowledge Time non-synchronous: in a large network, the same equip-discovery task will be formalized and the alarm weights will be ments usually can’t be standardized in common so that the timedetermined. Meanwhile, we design a binary neural network, of shows different. It is therefore no surprise that so many timewhich the input vector are some key elements that can represent errors can make the mining very difﬁcult.alarms. After the course of sample data training, alarms with the Alarm data, which are made of short messages, generally tex-similar weights will be divided into the same class. The weights tual formats, and typically several ﬁelds including informationof the neural network may not only reﬂect the knowledge of the creation time, location and some alarm conditions, can be con-experts but also change automatically when the input change. sidered inequity. These items should be given different weights This paper is organized as follows: In Section 2, we introduce to reﬂect their different importance.our system model and its operation process. Section 3 shows theexperimental platform and experimental results in telecommuni- From above analysis of the problems in alarm correlation anal-cation network environment. Finally a conclusion is drawn in ysis, it is well known that the extraction and the time synchroniza-Section 4. tion of alarms are two most important factors in data preprocessing. The methods to resolve the two problems will be provided in Section 3.2.2. Preprocessing experts system proposed 2.2. Alarm extraction and time synchronization2.1. Problem description of the original alarms All the analysis methods need history logs about alarm data. In the process of data preprocessing, we are interested in mak- However, we do not need any information about the topology ofing the original alarms clean and useful. The preprocessing in- networks. In this respect, the analysis system can be used in differ-cludes alarm data collection and data cleaning (cleaning means ent networks. We extracts alarm time, alarm level, alarm sort andadding with the missing data, discarding the redundant data and equipment address to form an alarm event and marks it as a 4-tu-reducing the volume of data). By preprocessing, we can convert ple (a, s, l, t).The four attributes are so important that can representoriginal alarm data into alarm transactions. the alarm. Fig. 1 shows the information extraction process. Alarms are short messages, generally of textual format, that are Time synchronization problem exists in the original alarms.symptomatic of a change in condition (often an abnormality) in a Some alarms may happen in one or two seconds, and sometimessystem. According to X.733 protocol of the ITU-T standard recom- some alarms even occur at the same time. The mining efﬁciencymendations, an alarm typically contain several ﬁelds giving infor- is too low to mine the original alarms directly. In order to deal withmation about the following attributes: Equipment name, device this problem, we should converse the original alarms into thetype, equipment address, interface types, alarm level, alarm types, appropriate alarm affairs by examining the original data over aalarm status, and alarm time, etc. user-speciﬁed time window. Unfortunately, the alarm does not usually contain signiﬁcantinformation of network fault. When a network fault appears, it will Deﬁnition 1. Given a set E, the alarm sequence S = {s, Ts, Te} is antrigger a series of alarms subsequently, but not all alarms have cor- ascending sequence occurring in the time interval [Ts, Te],relation with the fault. As a matter of fact, it is necessary to analyze Sw = {w, ts, te} is a time window of the sequence S, in which ts Ts,all alarms to ﬁnd out the relationship of alarms and determine the te Te, w ¼ fw # Sjt s t t e g. te À ts is the width of the window, asroot cause of the fault. W. As we know, original alarms from the actual network oftenmeet several major problems such as information redundancy,incomplete, time synchronization, a lot of noise that has no rela- Deﬁnition 2. Given the time window width W and the windowtionship with the association rules, and different attributes. Con- sliding step s, the starting time of the sequence is Ti, and the endingsidering these reasons, ARM-ACAS can not deal with the original time is Te, the time span Te À Ti = W is called the width of the win-alarms directly. In order to be suitable for data mining, original dow. The alarm in a window started at Ti + s and ended at Te + s.alarms must be converted into transactions and distributed with Fig. 2 shows the time widow, of which the alarm sequence isdifferent weights. In general, the main problems of the original {A, C, . . . , A, F}, the width of the window is 5 and the sliding stepalarms are described as follows: is 2. Deﬁne a transaction time interval to deal with the asynchro- A fault often triggers many alarms: It has been observed that in nous problem of alarms. The alarms with the values of ‘‘alarm most cases faults occur in bursts because any change of the time” within the same time window may be incurred by the same behavior of a single node of a complex network can perturb, fault. Combine all the alarms with the following conditions to form and therefore may cause faults in other nodes of the same net- an alarm transaction: work. As a result, equipment faults may occur intermittently and cause multiple alarms. (1) The values of alarm time are within the same time interval. An alarm typically contains many attributes, some of which are (2) The values of cleared time are within the same time interval. failed to mine the association rules. Only a part of the attributes (3) The orders of alarms in the time interval are the same.
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T.-Y. Li, X.-M. Li / Expert Systems with Applications 38 (2011) 1709–1715 1711 Fig. 1. Information extraction. four classes. The model can be seen as the simplest kind of feed- forward neural network which contains three inputs, six link weights, two neuron and two outputs. In this neural network model, three parameters should be con- ﬁrmed: (1) the ration of description for the inputs; (2) the link weights of the neural network and (3) the transfer function. Fig. 2. An example of the time window. In our study, input datasets are alarm data in telecommunica- tion network. Alarm attributes contain many factors, four of which including the node degree of the alarm equipment, alarm level that Over the time window operation, the original alarms will be may reﬂect the severity and the alarm type inﬂuence the telecom-converted into transactions. The ultimate goal of using time win- munication network most, so that they should be chosen as the in-dow is to improve the mining efﬁciency, to obtain the location puts of the neural network. The outputs of the neural network arequickly and to predict the severe network faults accurately. the alarm classes we need. Alarms with the similar importance will be divided into the same category, and different classes will have2.3. Neural network proposed for conﬁrming alarm weights different weight values. At ﬁrst, we input the sample values with the experience of experts. After training the link weights of neural An artiﬁcial neural network is an information processing para- network with two neurons, we will get the neural network model.digm that is inspired by the way biological nervous systems such The construction process of the neural network displays in Fig. 3.as the brain, process information. Neural network, with its remark- Select the sample {p1, q1}, . . . , {pn, qn}, where P1 = (a11, . . . ,able ability to derive meaning from complicated or imprecise data, a1m)T, . . . , Pn = (an1, . . . , anm)T (p is input vector, q is output vector), È Éwhich can be used to extract patterns and detect that are too com- deﬁne the original value of link weight as w0 ; . . . ; w0 . Alarm data 1 mplex to be noticed by either humans or other computer techniques. entry are multidimensional, the dimensional vectors can be ex- There are already many methods for weights analysis in mining pressed as n Â m. The link weights of the neural network can be ex-association rules, but they are unﬁt for the alarm weights conﬁr- pressed as m Â 1 dimension vectors, neural networks are designedmation in telecommunication networks. Using neural network by vector multiplication and the output dimensional vector can becan handle alarm weights well (Li Li, 2007). In this paper, we pro- expressed as n Â 1. Speciﬁcs as follows.pose a binary neural network to conﬁrm the weights of alarm data The inputs of the neural network are given as P1 = [a11, . . . , 2 3effectively. During the course of the neural network training, we a11 Á Á Á a1mcan determine a set of link weights which reduces the system error T T 6 . . 7 a1m] , . . . , Pn = [an1, ... , anm] , where anÂm ¼ 4 . . . 5¼ .as close to zero as possible. In this case, relevant data are entered an1 Á Á Á anminto the neural network in order to identify patterns automatically. 2 T3When the neural network has been trained successfully, we can P1 6 . 7 Tuse it to determine the alarm weights. 4 . 5:The link weights are shown as WmÂ1 = [w1, ... , wm] . . PT n2.3.1. Design the neural network Pure inputs of the neural network are written as In the neural network, the inputs have three key factors which n = a Á W + b = [P1, . . . , Pn] Á [w1, . . . , wm] + b.inﬂuence the alarm weights, and the outputs are different classiﬁ- The output can be written as Q ¼ f ðnÞ ¼ f ða Á W þ bÞ ¼ f ðanÂm Ácations of alarms. Considering a binary neuron neural network, the W mÂ1 þ bÞ ¼ f ð½P 1 ; . . . ; Pn Á ½w1 ; . . . ; wm þ bÞ;, where f shows thelearning process can be accomplished to divide the alarms into transfer function of the neural network, b is the external input. Fig. 3. Binary neural network classiﬁers.
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1712 T.-Y. Li, X.-M. Li / Expert Systems with Applications 38 (2011) 1709–1715 Output vectors Q1, Q2 have two values À1, 1, respectively. Input target value, and the adjustment rule can be shown as 8vectors can be divided into four classes: (À1, À1) (À1, 1) (1, À1) e 0 Y new ¼ Y old þ X(1, 1), and sample values are caught with the experience. After e 0 Y new ¼ Y old À X ; let q ¼ ge ¼ f 1 e ¼ 0; fracekeke – 0; :; :classiﬁed by the neural network, alarms with the similar impor- e ¼ 0 Y new ¼ Y oldtance will get the same weights. it is proved that if there exists a weight value, it can be converged to How to choose the transfer function is a crucial step in the neu- the expectant value.Weight vector after kth iteration isral network design process. In our design, according to the charac- YðkÞ ¼ Yðk À 1Þ þ X 0 ðk À 1Þ ð4Þteristics of alarm data transmission, the input value is set as À1 or1, and the transfer function can be chosen as hard limiting function 0 in which X (k À 1) is an element of the following set:hardlim. {X1, X2, . . . , XQ, ÀX1, . . . , ÀXQ}.Assume that the vector can classify The hard limiting function hardlim is given as the Qth input correctly, described as Y*, assume that À1; v 0 tq ¼ 1; XÃT Xq d 0 ð5Þf ðv Þ ¼ ð1Þ 1; v P0 tq ¼ 0; ÃT X Xq Àd 0 ð6Þ In order to prove the convergence of the rules, the upper and lower2.3.2. Training process of the link weights limits of each vector are needed. After the course of samples studying and link weights training, Set the initial weight vector 0, as Y(0) = 0.the data will achieve linear classiﬁed. Training the link weights of Weight after kth iteration isneural networks includes the steps described below. Let a(n) de-note input vectors, Q(n) represent the actual output vectors; q(n) YðkÞ ¼ Yðk À 1Þ þ X 0 ðk À 1Þ ¼ X 0 ð0Þ þ X 0 ð1Þ þ Á Á Á þ X 0 ðk À 1Þ ð7Þare output values in theory; g means learning step, which is a po- Inner product can be concluded assitive integer below 1 (the descriptions of the symbols are as thesame as before). Y ÃT YðkÞ ¼ Y ÃT X 0 ð0Þ þ Y ÃT X 0 ð1Þ þ Á Á Á þ Y 0T X 0 ðk À 1Þ: ð8Þ (1) Choose the sample data {p1, q1}, . . . , {pn, qn}, where *Y ÃT X q d P1 = (a11, . . . , a1m)T, . . . , Pn = (an1, . . . , anm)T (p denote the input elements, q denote the output elements). )Y ÃT X 0 ðjÞ d È É (2) Setting the initial values of the link weights as w0 ; . . . ; w0 . 1 m )Y ÃT YðkÞ kd ð9Þ (3) In step n (n = 0, 1, 2, . . .), input the vector a(n), calculate the actual output Q(n) = f(a(n) + b). By the Cauchy–Schwarz inequality, (4) Adjustment rules of the link weights, in which e denotes the system error: ðY ÃT YðkÞÞ2 6 kY ÃT k2 kYðkÞk2 ð10Þ wðn þ 1Þ ¼ wðnÞ þ g½qðnÞ À Q ðnÞaðnÞ ð2Þ in which ||Y(k)||2 = YT(k)Y(k).From (9) and (10) we can conclude that e ¼ qðnÞ À Q ðnÞ ð3Þ ðY ÃT YðkÞÞ2 ðkdÞ2 (5) Set n = n + 1, if |e| P e, back to the step (4), else if |e| e, end kYðkÞk2 P Ã 2 : ð11Þ kY k kY Ã k2 (e is a speciﬁed small positive value). *kYðkÞk2 ¼ Y T ðkÞYðkÞ Adjust the link weights by making the error achieve the system ¼ ½Yðk À 1Þ þ X 0 ðk À 1ÞT ½Yðk À 1Þ þ X 0 ðk À 1Þerror tolerance e, and then repeat the process of training until all ¼ Y T ðk À 1ÞYðk À 1Þ þ 2Y T ðk À 1ÞX 0 ðk À 1Þ ð12Þthe patterns are trained completely. The training process of the linkweights is shown in Fig. 4. þ X 0T ðk À 1ÞX 0 ðk À 1Þ: When the classiﬁcation is wrong, the weights need update, and2.3.3. Convergence analysis of the design in this case the two symbols are contrary, so as to YT(k À 1)X0 (k À 1) 6 0. So (12) can be simpliﬁed to ||Y(k)||2 6 ||Y(k À 1)||2 + ||X0 (k À 1)||2.After repeated iterations, it isProof. Select sample data as {p1, q1}, . . . , {pn, qn}, in which the given byexpectation of output are qn with the value 1 or À1Let X = [P b]T bethe input vector, where b is an external input value. Here set the link kYðkÞk2 6 kX 0 ð0Þk2 þ kX 0 ð1Þk2 þ Á Á Á þ kX 0 ðk À 1Þk2 ð13Þweight vector Y = [W1]T, make 1 as its offset value. It is well known 2that the pure input of the neural network is n = PTW + b = XTY and 0 Let u = max{||X (j)|| }, and it can satisfythe update rule of the link weights is described as Ynew = Yold + Xq, kYðkÞk2 6 k/ ð14Þlet e = q(n) À Q(n) be the error of the true output value and the Fig. 4. Training process of the link weights.
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T.-Y. Li, X.-M. Li / Expert Systems with Applications 38 (2011) 1709–1715 1713 Fig. 5. Preprocessing expert system model. The upper and lower limits of weight vector can be denoted as 2.4. Preprocessing expert system modelingðkdÞ2 Based on the above descriptions, our proposed preprocessing kYðkÞk2 6 k/: ð15ÞkY Ã k2 expert system model maintains two parts: the time window pro- cessing and the neural network processing. In this whole system,Therefore we have time window processing module handles the original alarms ﬁrst, /kY Ã k2 and then input the cleaned data into the neural network to getk : ð16Þ their weights. Fig. 5 shows the working process of the preprocess- d2 ing expert system.Eq. (16) proves that the count of updating is limited, therefore the From this ﬁgure, we can see that alarm processing expert sys-training algorithm is convergence. It shows that after limited times tem is an expert system which is based on the rules, and each mod-training, we will get the neural network we need. Proof is end. h ule has its own man-machine interface. Fig. 6. The topology of the network.
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1714 T.-Y. Li, X.-M. Li / Expert Systems with Applications 38 (2011) 1709–1715Table 1The preprocessing of the alarms. Time window is 5 s, the sliding step is 3 s The number of original alarms 1000 2000 3000 4000 5000 6000 7000 8000 9000 10,000 The number of transactions 350 715 1064 1372 1739 2077 2404 2728 3093 3538 Time window is 10 s, the sliding step is 3 s The number of original alarms 1000 2000 3000 4000 5000 6000 7000 8000 9000 10,000 The number of transactions 369 721 1041 1369 1716 2084 2396 2784 3106 3482 Time window is 10 s, the sliding step is 6 s The number of original alarms 1000 2000 3000 4000 5000 6000 7000 8000 9000 10,000 The number of transactions 166 355 523 678 875 1037 1205 1384 1547 1725 The time window part has four Independent modules, and each Preprocessing process works on the simulated alarm datasets. Inmodule has its own rules. For example, time synchronization is the experiments, we selected three time windows to deal with dif-handled by the rules of the time synchronization, and extracting ferent number of original alarms ranging from 1000 to 10,000, andalarm transactions must follow the rules of extracting alarm trans- then the transactions would be generated and stored to mine theactions. Meanwhile, the neural network processing part is in accor- association rules. After the preprocessing, we reduced more thandance with the rules for setting the alarm weight. half number of the original alarms. Table 1 shows not only the pre- The rules of the system shows as P P Q or IF P THEN Q, in which processing results of the original alarms, but also how great theP is the prerequisite and Q is the conclusion. For the whole system, time window width have inﬂuence on a number of frequent items.the rules of each part are given by the network management ex- In the ﬁrst test, we set the time window for 5 s and sliding step forpert. For example, the sliding step and the time window width 3 s. Original alarms can be converted to smaller number of transac-are given by the experience of the experts for different networks, tions by the preprocessing expert system. From the table we canand the learning samples of the neural network are also set by ﬁnd that the number of transactions is nearly 1/3 of the originalthe experts. alarms. For example, when the number of original alarms is up to As we know, the preprocessing expert system is an important 10,000, we have only 3538 transactions. In the second test, wepart of the whole mining system, for it can provide clean and change the time window to 10 s, but keep the sliding step 3 s. Com-appropriate data to ﬁnd alarm association rules. parison of the ﬁrst test indicates that when the alarms have small number, the number of transactions is little bigger than the ﬁrst3. Experiments and results testing. But, when the number of original alarms increases, the dif- ference is not obvious. In the ﬁnal test, we double the value of the3.1. The experimental setup time window and the sliding step used in the ﬁrst test. Correspond- ingly, the number of transactions is almost half of the ﬁrst test. In A series of experiments have been done to show the perfor- association rules mining, the number of transactions in the thirdmance of our system on AMD Sempron (tm) Processor 2800+ ma- test is too small to ﬁnd enough correlated rules. With comprehen-chine with 512MB of main memory, running Microsoft Windows sive consideration, we decide to use the ﬁrst test setting in our sim-XP Professional operation system. All codes and interfaces are writ- ulation. Because the results show that when the window is 5 s, andten in JAVA. We can get the alarm data from the simulated tele- the sliding step is 3 s, the number of transaction changes very sta-communication network in some principles. Fig. 6 shows the ble. In this situation, we will get enough transactions to ﬁnd associ-topology of real-world network with twenty nodes, there are three ation rules and make sure the pretreatment has high efﬁciency.root nodes 1, 10, 18 among them, while alarms of other nodes aretriggered by these three root nodes. The bandwidth of root nodesare 8M, the other link bandwidth is 2M and the link is ﬁnally con- 3.3. The test of training neural networknected to the entire CHINANET with 100M bandwidth. In order to determine the alarm weights, we must ﬁrst sort the3.2. Simulation principle alarm data. Classiﬁcation is based on the three important attri- butes of the alarms: the node degree of network equipment, the A method for simulating the occurrence of alarms is also incor- alarm level and the alarm type. Before input the data to the neuralporated in the simulation. We construct the network and produce network, the three attributes of the alarms must be processed intoalarms in order to make sure that our algorithm is correct and efﬁ- a triple (a, s, l), and the description attributes will be quantiﬁed. Gi-cient. Based on the characteristics of telecommunication networks, ven a topology structure of the communication network, we canwe generate original alarms using the following principles: get the node degree, and then classify the alarm into 4 levels: (1) serious alarm, quantiﬁed as 1; (2) major alarm, quantiﬁed as 2; Alarms in lower level of arbitrary node cause corresponding (3) minor alarm, quantiﬁed as 3; (4) indicative alarm, quantiﬁed alarms in upper lever of the same node. as 4. According to the network topology, the node which is directly Alarms of the edge node may be transmitted to the center node linked to the root node and has more than 4 node degree generates which connects with a probability p. serious alarms; the node generates major alarms when it directly Alarms of the center node will be transmitted to one of the edge links to the root nodes and has the number of node degree between node which connects randomly. 2 and 4; the node generates minor alarms must meet one of the Alarms of the center node will be transmitted to all center following conditions: has less than 2 node degree but directly links nodes which they connect with. to the root node, or has more than 3 node degree but indirectly links to the root node; the node generate indicative alarms in other Using above principles, the original alarms can reﬂect relation- situations. Alarm types are divided into ﬁve categories: (1) com-ships of network elements truthfully. In this case, the alarm gener- munication alarms, quantiﬁed as 1; (2) device alarm, quantiﬁedated from edge node may have correlation with the alarms that as 2; (3) environmental alarms, quantiﬁed as 3; (4) running alarm,generated from the center node. quantiﬁed as 4; (5) service alarm, quantiﬁed as 5.
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T.-Y. Li, X.-M. Li / Expert Systems with Applications 38 (2011) 1709–1715 1715 This neural network has three layers: the ﬁrst layer is the input work topology can be rapid realized by adjusting thelayer. On this layer, the vectors are input to the neural network weights so as to make the weighted association rules miningafter quantiﬁed according to the principle of the quantiﬁcation; more scientiﬁc and effective.the second layer is the middle layer of the neural network, and itconsists two neurons; the third layer is the output layer, output Experimental results show that this preprocessing expert sys-the values of the classiﬁcation. According to the various attributes tem plays a most important part in the whole mining process forof the alarms, we divide the alarms into 4 categories with the value ﬁnding association rules and locating the root cause of faults.of 0.1, 0.2, 0.3 and 0.4. The neural network is convergence whenthe learning error square of the sample is less than 0.0001. After Acknowledgment47 iterations, the test meets the convergence condition and theneural network has been constructed completely. Finally, we can This work is supported by Natural Science Foundation of Chinaget the weights of all the alarms. (NSFC 60572091).4. Conclusions References Agrawal, R., Srikant, R. (1994). Fast algorithm for mining association rules. In The application of association rules mining in telecommunica- Proceeding of the 20th VLDB conference (pp. 487–499).tion network is an important area. In the special telecommunica- Amani, N., Fathi, M., Dehghan, M. (2005). A case-based reasoning method fortion environment, fault management and alarm correlation alarm ﬁltering and correlation in telecommunication networks. In Proceeding of the Canadian conference on electrical and computer engineering (pp. 2182–2186).analysis are critical but difﬁcult tasks, for a large number of alarms Bouloustas, A. T., Calo, S. B., Finkel, A. (1994). Alarm correlation and faulthave their own characteristics. Therefore, dealing with these identiﬁcation in communication networks. IEEE Transactions onalarms ﬂexibly and automatically are necessary and practical. Communications, 42(234), 523–533. Hatonen, K., Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H. (1996).The preprocessing expert system proposed in this paper is based TASA: Telecommunication alarm sequence analyzer or how to enjoy faults inon the time window technology and the neural network technol- your network. In Proceeding of IEEE network operations and managementogy. Different from the traditional expert systems, our system is symposium, 1996 (pp. 520–529).thought to be a more ﬂexible approach with higher operating efﬁ- Hatonen, K., Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H. (1996). Knowledge discovery from telecommunication network alarm databases. Inciency. By using our system, we can quickly change the original Proceeding of 12th international conference on data engineering (ICDE’96), Newalarms into the proper form that we need for the association rules Orleans (pp. 115–122).mining. Totally speaking, this preprocessing expert system has fol- Hou, S. Z., Zhang, X. F. (2008). Analysis and research for network management alarms correlation based on sequence clustering algorithm. In Proceeding oflowing improvements and innovations: ICICTA’2008 (pp. 982–986). Klemettinen, M., Mannila, H., Toivonen, H. (1999). Rule discovery in (1) In telecommunication networks, alarms have quantities and telecommunication alarm data. Network and Systems Management Journal, 7(4), 395–423. dynamic changes. Preprocessing process helps turn the Li, T. Y., Li, X. M. (2007). The study of the neural network applied to weighted alarms to the uniﬁed framework which is suitable for min- association rules mining. In Proceedings of the international conference ing. There are a lot of things to do in this process, of which ICWAPR2007 (pp. 742–745). Marilly, E., Aghasaryan, A., Betgé-Brezetz, S., O’Martinot, O., Delègue, G. (2002). extracting useful elements of alarms and selecting proper Alarm correlation for complex telecommunication networks using neural time window to resolve the original alarms are most networks and signal processing. In IEEE workshop on IP operations and important. management (pp. 3–7). Ng, R., Lakshmanan, L. V. S., Han, J., Pang, A. (1998). Exploratory mining and (2) The weight determination can inﬂuence the mining efﬁ- pruning optimizations of constrained association rules. In Proceedings of the ciency. In this presentation, we have described a feed-for- SIGMOD’98 (pp. 13–24). ward neural network model to determine the alarm Sarawagi, S., Thomas, S., Agrawal, R. (1998). Integrating association rule mining weights according to the features of alarms and telecommu- with relational database system: Alternatives and implications. In Proceedings of the SIGMOD’98 (pp. 343–354). nication network topologies. Based on some typical sample Srikant, R., Vu, Q., Agrawal, R. (1997). Mining association rules with item values of an actual network, the link weights of neural net- constraints. In Proceedings of the KDD’97 (pp. 67–73). work model have been ﬁxed to stable values after training Tang, Y. N., Al-Shaer, E., Boutaba, R. (2008). Efﬁcient fault diagnosis using incremental alarm correlation and active investigation for internet and overlay certain times. The application of neural network to deter- networks. IEEE Transactions on Network and Service Management, 5(1), 36–49. mine the alarm weights can not only reﬂect the experience Weiss, G. M., Hirsh, H. (1998). Learning to predict rare events in event sequences. and knowledge of experts, but also make full use of the In Proceedings of the 4th international conference on knowledge discovery and data mining (pp. 359–363). AAAI Press. alarm attributes accurately. Meanwhile, the changes of net-
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