Abstract:
There is provided a method of fault management of a smart device including comparing a value of a fault detection indicator (hereinafter referred to as ‘FDI’) in a normal state, which detects faults generated in the smart device, with respect to at least one performance indicator, with an FDI value observed in real time and detecting the faults by calculating a relative variation level of the observed values, and creating a diagnosis object (hereinafter referred to as ‘DO’) including a cause and a countermeasure of the detected fault and analyzing the fault.

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims the benefit under 35 U.S.C. §119(a) of Korean Patent Application No. 10-2012-0117988, filed on Oct. 23, 2012, the entire disclosure of which is incorporated herein by reference for all purposes. 
     BACKGROUND 
     1. Field 
     The following description relates to technology to detect and analyze faults of an electronic device, and more particularly, to a device that can automate fault detection and analysis of a smart device. 
     2. Description of the Related Art 
     Recently, personal terminals designed to perform only a specific function, for example, a music file player, an e-Book reader, an electronic dictionary, and a mobile phone, are being replaced by smart devices that actually perform PC functions. Therefore, a variety of services based on mobile applications, for example, universal device synchronization and file sharing, are becoming commercialized and common. Terminals having sophisticated functions with a variety of sizes and specifications have been released. 
     However, despite the popularity of such smart devices, development of terminal management technology has not significantly progressed. In particular, a terminal fault needs to be automatically managed in real time. However a variety of software fault problems can occur in a terminal device, and it is difficult for a personal user to analyze the terminal fault and to address associated problems directly. 
     Meanwhile, as the smart device is recognized as an important network element, remote terminal management technology has become a big issue for smart device manufacturers as well as application service providers for smart devices. However, operators have a big burden of costs in terms of capital expenditures (CAPEX) and operational expenditures (OPEX) with a conventional passive type of terminal management. Accordingly, it is urgent to provide an automated terminal fault management framework for terminal-based service markets. 
     Meanwhile, in order to overcome limitations on passive analysis methods that depend on a service operator for determining abnormalities of the terminal, a method in which static rules or policies were defined and faults were accordingly detected and analyzed based on If/Else statements has been mainly applied conventionally. However, such conventional methods have a problem in that the number of rules becomes massive when a size of networks configured with terminals increases. 
     As an alternative method, a method in which a separate threshold is set to an individual performance indicator and the fault is determined by merely observing breach of the threshold has been proposed. However, in reality, it is difficult to set appropriate thresholds, and it has a disadvantage in that the threshold value needs to be continuously recalibrated according to states of the terminal and the network even when an initial threshold was accurate. 
     As an improved method, a method based on pattern matching is being studied but has a problem in that it is difficult to apply determination formulas and it needs a large amount of calculation to determine the fault. 
     Moreover, the above-described three methods have a disadvantage in that they include many errors in detection and analysis since they are based on binary decision to determine whether there is a fault. That is, since those detection techniques based on simplified information have structural vulnerability causing information loss, they have low effectiveness when applied to actual systems. 
     SUMMARY 
     The following description relates to a device and a method for fault management of a smart device that can minimize intervention of a service operator by supporting an automated detection and analysis procedure of a terminal fault. 
     Moreover, the following description relates to a device and method for fault management of the smart device that can minimize errors due to fragmented analysis. 
     In one general aspect, a method of fault management of the smart device includes comparing a value of a fault detection indicator (hereinafter referred to as ‘FDI’) in a normal state, which detects faults generated in the smart device, with respect to at least one performance indicator, with an FDI value observed in real time and detecting the faults by calculating a relative variation level of the observed values, and creating a diagnosis object (hereinafter referred to as ‘DO’) including a cause and a countermeasure of the detected fault and analyzing the fault. 
     In another aspect, a device for fault management of the smart device includes an FDI level calculating unit configured to compare a value of a fault detection indicator (hereinafter referred to as ‘FDI’) in a normal state, which detects faults generated in the smart device, with respect to at least one performance indicator, with an FDI value observed in real time and detect the faults by calculating a relative variation level of the observed values, an association degree checking unit configured to check association with a diagnosis object (hereinafter referred to as ‘DO’) including a cause and a countermeasure of the detected fault, and a relevance estimating unit configured to compare an association value checked by the association degree checking unit and create a DO having a maximum relevance value. 
     Other features and aspects will be apparent from the following detailed description, the drawings, and the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is diagram illustrating a general structure of a fault detection and analysis service. 
         FIG. 2  is a diagram illustrating a device for fault management of a smart device according to an embodiment of the invention. 
         FIG. 3  is a diagram illustrating a method of fault management of the smart device according to the embodiment of the invention. 
         FIG. 4  is a flowchart for describing FDI profile generation operations according to the embodiment of the invention. 
         FIG. 5  is a flowchart for describing fault detection operations based on an FDI level according to the embodiment of the invention. 
         FIGS. 6A and 6B  are flowcharts for describing fault analysis operations according to the embodiment of the invention. 
         FIG. 7  is a graph for describing association checking according to the embodiment of the invention. 
     
    
    
     Throughout the drawings and the detailed description, unless otherwise described, the same drawing reference numerals will be understood to refer to the same elements, features, and structures. The relative size and depiction of these elements may be exaggerated for clarity, illustration, and convenience. 
     DETAILED DESCRIPTION 
     The following description is provided to assist the reader in gaining a comprehensive understanding of the methods, apparatuses, and/or systems described herein. Accordingly, various changes, modifications, and equivalents of the methods, apparatuses, and/or systems described herein will be suggested to those of ordinary skill in the art. Also, descriptions of well-known functions and constructions may be omitted for increased clarity and conciseness. 
     Hereinafter, exemplary embodiments will be described in detail with reference to the accompanying drawings. The following exemplary embodiments should be considered in a descriptive sense only to understand sprit of the invention and the scope of the invention is not limited by the embodiments. 
     In general, when a value, an operation, or a pattern suspected as a fault is detected in various smart devices, a service operator directly intervenes and passively analyzes, for example, accuracy of fault detection, identification of causes and countermeasures in order to verify the fault. This procedure will be described with reference to  FIG. 1 . 
       FIG. 1  is diagram illustrating a general structure of a fault detection and analysis service. 
     As illustrated in  FIG. 1 , a fault management DB  10  is a set of reference data recording, for example, a value, an operation, and a pattern confirmed as a fault based on previous data determined as an actual fault. 
     A service operator  20  refers to an operator who manages a service or a network and analyzes whether or not the detected fault is an actual fault, a cause of fault generation, and a countermeasure according to the fault generation cause. 
     An IP network  30  generally refers a communication network including the Internet based on a TCP/IP protocol. 
     A smart device  40  refers to a personal terminal held by a user and includes a smart phone, a tablet PC, and a variety of smart devices having a type of tab. Most services and applications operated in a conventional PC may be operated in such smart devices. 
     Fault detection  50  may be performed by a passive monitoring method in which the smart device  40  informs the service operator  20  of a specific fault or an active monitoring method in which the service operator  20  can directly detect whether or not there is a fault in each smart device  40 . 
     Passive analysis  60  refers to a process in which the service operator  20  directly intervenes and passively analyzes content of the fault. 
     Repair performance  70  refers to remotely performing the countermeasure for addressing the cause of the content confirmed as the fault. Since the remote fault repairing may be difficult according to a state of the smart device, it may be optionally performed. 
     As illustrated in  FIG. 1 , however, the passive smart device fault detection and analysis method have limitations in managing a variety of software fault problems since a single device can process a variety of applications. Therefore, there are provided a device and method that can automatically manage the terminal fault. 
       FIG. 2  is a diagram illustrating a device for fault management of the smart device according to an embodiment of the invention. 
     As illustrated in  FIG. 2 , a fault management device  200  of the smart device refers to a management framework that performs overall processes of fault detection and analysis of a smart device  100 . 
     A network interface  210  is a network interface for transmitting and receiving data to deliver FDI values observed in the smart device  100  to the fault management device  200 . 
     An FDI collector  220  collects FDI observation values from the smart device  100  and may be configured with a plurality of FDIs including FDI 1  to FDIM. Here, the FDI refers to a fault detection indicator. As an example of the FDI, performance indicators, for example, network delay, channel bandwidth, CPU load, and battery consumption of a device, may be considered. Therefore, the FDI may be defined as a set including a plurality of performance indicators for fault detection described above. 
     An FDI level calculating unit  230  calculates levels of the FDI values obtained from the FDI collector  220 . Here, the FDI level refers to a value quantifying how much variation is represented in an observed FDI normal distribution model of a current smart device compared to an FDI normal distribution model in a normal state. That is, an amount of difference between the FDI profile value generated in the normal state of the smart device  100  and current collected FDI value is calculated in terms of the normal distribution model. This will be described in detail below with reference to  FIG. 5 . 
     A repair listing unit  240  retrieves all repairs including a specific DO (for example, an x-th DO: DO x ) from a repair database  290  and sorts them with specific acquisition criteria (for example, date of repair generation). 
     Here, the repair includes a pair of a subset of the FDI and a DO, in the form of (Repair i =(f j   sub , DO x )). Repair, represents an i-th repair including DO x , where i=1, 2, 3, . . . , and R. Further, f j   sub  represents a j-th subset of the FDI, where j=1, 2, 3, . . . , 2|FDI|−1. DO x  represents an x-th DO, where x=1, 2, 3, . . . , Q. 
     Here, the DO x  includes root cause(s) or action(s). The root cause(s) refers to major cause(s) of corresponding fault generation. The action(s) refers to a countermeasure(s) for addressing a corresponding fault. For example, the DO x  may be configured with a form of “DO x (Root Cause(s))”, “DO x (Action(s))”, or “DO x (Root Cause(s), Action(s)).” In the third form of the DO x , it is assumed that relation between root cause(s) and action(s) may be previously configured in accordance with conventional methods, general fault cause(s) and solution(s) thereof. 
     Moreover, since different f j   sub  can be paired with the same DO x , the repair including the DO x  is not limited to one but may be plural. 
     An association degree checking unit  250  determines which DO has the highest association with an individual FDI. For this purpose, a relative occurrence frequency of each FDI in all repairs including a corresponding DO is calculated. This calculation is repeated until calculation of the association value of all FDIs in which abnormal variations are observed in all repairs including the specific DO is completed. This will be described in detail below with reference to  FIGS. 6A and 6B . 
     A relevance estimating unit  260  searches for a DO having the highest association with the latest observed FDI value and estimates cross-relevance using an FDI level value calculated by the FDI level calculating unit  230  and an association value calculated by the association degree checking unit  250 . That is, the FDI level value means a variation difference with respect to the profile and the association value serves as a weighted value. This will be described in detail below with reference to  FIGS. 6A and 6B . 
     A profile generating unit  270  identifies an average value and a variance value of each FDI value, and which form of probability distribution model is close to a corresponding FDI value based on data observed when the smart device  100  is in a normal state, that is, in a faultless state, and generates an FDI profile based on those three pieces of data. In particular, since a plurality of FDI profiles may be generated with respect to the same FDI depending on, for example, a target FDI, a measured time zone, a target device, a target application, and a target event, accuracy of the fault analysis may be increased. This will be described in detail below with reference to  FIG. 4 . 
     An FDI profile DB  280  refers to a database managing the plurality of FDI profiles generated by the profile generating unit  270 . In the present invention, it is assumed that such a database is prepared in advance. The FDI profile DB  280  may be detected and used by the FDI level calculating unit  230 . 
     A repair DB  290  is a database storing and managing a plurality of Repair i =(f j   sub , DO x ) with respect to a case determined as the actual fault. The repair DB  290  may be detected and used by the repair listing unit  240 . 
     A repair manager  300  creates, in advance, and manages the FDI in which the variation was found, a major cause (root cause) leading the variation of the FDIs, and the countermeasure (action) to address the cause with respect to the case determined as the actual fault in a form of repair. 
       FIG. 3  is a diagram illustrating a method of fault management of the smart device according to the embodiment of the invention. 
     As illustrated in  FIG. 3 , the method of fault management of the smart device includes a fault detection operation (S 10 ) and a fault analysis operation (S 20 ) 
     As a result of the fault detection operation (S 10 ), the fault may be confirmed, and as a result of the fault analysis operation (S 20 ), a compliant DO may be output. 
     Specifically, the fault detection operation (S 10 ) is performed through an FDI level calculation (S 11 ). This will be described in detail below with reference to  FIG. 5 . 
     Although not illustrated in drawings, an FDI profile generation that previously prepares the performance indicator corresponding to the individual FDI should be performed in advance to the FDI level calculation (S 11 ). This will be described in detail below with reference to  FIG. 4 . 
     Specifically, the fault analysis operation (S 20 ) includes sub-operations, repairs listing (S 21 ), association checking (S 22 ) and relevance estimation (S 23 ). 
     The repairs listing (S 21 ) includes obtaining all repairs including the specific DO (for example, an x-th DO: DO x ) and sorting them based on specific criteria (for example, date of repair generation). 
     The association checking (S 22 ) includes analyzing an association relation between the latest observed FDI value based on the fault detection time and each repair including the specific DO x , and determining relevance of DO x  with respect to corresponding fault. 
     The relevance estimation (S 23 ) includes estimating a degree of relevance (or suitability) of the specific DO x  configured with the root cause(s) and the action(s) as the cause analysis and the countermeasure corresponding to the fault. Moreover, the relevance estimation (S 23 ) is a process in which the DO x  having the highest relevance value is matched with the most appropriate cause analysis and countermeasure for the corresponding smart device or a specific event of corresponding smart device. 
     The association checking (S 22 ) and the relevance estimation (S 23 ) will be described below in detail with reference to  FIGS. 6A and 6B . 
     That is, the invention relates to technology for selecting the DO x  that makes an optimum correspondence (pair-wise) with a specific fault. Finally, the service operator may obtain the most appropriate DO x  with respect to the corresponding fault. 
       FIG. 4  is a flowchart for describing FDI profile generation operations according to the embodiment of the invention. 
     As illustrated in  FIG. 4 , in operation  410 , the repair manager  300  or the operator previously prepares the performance indicator corresponding to the individual FDI. 
     In operation  420 , the profile generating unit  270  observes FDI values for a long time while each performance indicator is in a normal state. Associated FDI values are expressed as being measured k times at time intervals of n as in the following Formula 1. 
     
       
         
           
             
               
                 
                   
                     
                       
                         x 
                         1 
                       
                       , 
                       … 
                       ⁢ 
                       
                           
                       
                       , 
                       
                         x 
                         n 
                       
                     
                     
                       ︸ 
                       
                         a 
                         1 
                       
                     
                   
                   , 
                   
                     
                       
                         
                           x 
                           
                             n 
                             + 
                             1 
                           
                         
                         ⁢ 
                         
                             
                         
                         ⁢ 
                         … 
                       
                       ⁢ 
                       
                           
                       
                       , 
                       
                         x 
                         
                           2 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           n 
                         
                       
                     
                     
                       ︸ 
                       
                         a 
                         2 
                       
                     
                   
                   , 
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                     … 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       
                         
                           x 
                           
                             
                               
                                 ( 
                                 
                                   k 
                                   - 
                                   1 
                                 
                                 ) 
                               
                               ⁢ 
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                             + 
                             1 
                           
                         
                         , 
                         … 
                         ⁢ 
                         
                             
                         
                         , 
                         
                           x 
                           kn 
                         
                       
                       
                         ︸ 
                         
                           a 
                           k 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   1 
                   ) 
                 
               
             
           
         
       
     
     In Formula 1, x 1  represents a first observation value of the individual FDI and a total number kn of observation values may be derived. a 1  represents an average value of data observed from x 1  to x n  and k average values of data may be derived. 
     In operation  420 , the profile generating unit  270  extracts k average values from observation values x 1  to x kn , and calculates an average value and a variance value using Formula 2 and Formula 3 given below. 
     
       
         
           
             
               
                 
                   
                     
                       μ 
                       c 
                     
                     ⁡ 
                     
                       ( 
                       Y 
                       ) 
                     
                   
                   = 
                   
                     
                       1 
                       k 
                     
                     ⁢ 
                     
                       
                         ∑ 
                         
                           i 
                           = 
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                         k 
                       
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       
                         a 
                         i 
                       
                     
                   
                 
               
               
                 
                   ( 
                   2 
                   ) 
                 
               
             
           
         
       
     
     In Formula 2, μ c (Y) represents an arithmetic mean value from a 1  to a k  with respect to a specific c-th FDI and Y represents a random variable indicating a data observation value. 
     
       
         
           
             
               
                 
                   
                     
                       σ 
                       c 
                       2 
                     
                     ⁡ 
                     
                       ( 
                       Y 
                       ) 
                     
                   
                   = 
                   
                     
                       1 
                       k 
                     
                     ⁢ 
                     
                       
                         ∑ 
                         
                           i 
                           = 
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                         k 
                       
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       
                         
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                               a 
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                               ⁡ 
                               
                                 ( 
                                 Y 
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                           ) 
                         
                         2 
                       
                     
                   
                 
               
               
                 
                   ( 
                   3 
                   ) 
                 
               
             
           
         
       
     
     In Formula 3, σ c   2 (Y) represents a variance value from a 1  to a k  with respect to the c-th FDI. 
     Here, if xi observation samples are independent and identically distributed (I.I.D), arbitrary a j  follows a normal distribution as n increases infinitely. Therefore, when each x i  sample is obtained from different users or from different events of the same user device, x i  is also I.I.D, and the random variable Y also follows the normal distribution having the value of Formula 2 and Formula 3. 
     In operation  430 , the profile generating unit  270  identifies the most appropriate probability distribution model to the individual FDI from the observation value. 
     In operation  440 , the profile generating unit  270  determines whether a condition is changed. This is for generating the plurality of profiles with respect to the same FDI by repeatedly performing operations  430  and  440  under different conditions, for example, a measured time zone, a target device, a target application, and a target event. 
     When the condition is changed in operation  440 , the profile generating unit  270  proceeds to operation  430 . On the other hand, when the condition is not changed in operation  440 , that is, when operations  430  and  440  are performed in all possible conditions, the profile generating unit  270  proceeds to operation  460 . 
     In operation  460 , the profile generating unit  270  stores created profiles in the FDI profile DB  280 . 
       FIG. 5  is a flowchart for describing fault detection operations based on an FDI level according to the embodiment of the invention. 
     As illustrated in  FIG. 5 , hardware and software of the smart device  100  are activated in operation  510 . 
     In operation  520 , the FDI collector  220  of the fault management device  200  of the smart device monitors the latest values of all FDIs of the smart device  100 . 
     In operation  530 , the FDI collector  220  of fault management device  200  of the smart device calculates an average of the latest observation values of a specific FDI from FDI 1  to FDIM at a predetermined interval. This is expressed by following Formula 4 
     
       
         
           
             
               
                 
                   … 
                   ⁢ 
                   
                       
                   
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                   4 
                   ) 
                 
               
             
           
         
       
     
     In Formula 4, x t−n , x t−n+1 , . . . , x t  represents the latest observed data values of the specific c-th FDI and X c  represents an average value of the latest observed values of the specific c-th FDI. 
     In operation  540 , the FDI level calculating unit  230  of the fault management device  200  of the smart device obtains an appropriate profile by retrieving profiles from the FDI profile DB  280  in consideration of time zone of the observed c-th FDI, the target application service or the smart device. 
     In operation  550 , the FDI level calculating unit  230  of the fault management device  200  of the smart device changes the calculated average Xc to a random variable Zc of a specific probability distribution model using following Formula 5. 
     
       
         
           
             
               
                 
                   
                     Z 
                     c 
                   
                   = 
                   
                     
                       ( 
                       
                         
                           X 
                           1 
                         
                         - 
                         
                           
                             μ 
                             C 
                           
                           ⁡ 
                           
                             ( 
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                             ) 
                           
                         
                       
                       ) 
                     
                     
                       
                         σ 
                         c 
                       
                       ⁡ 
                       
                         ( 
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                         ) 
                       
                     
                   
                 
               
               
                 
                   ( 
                   5 
                   ) 
                 
               
             
           
         
       
     
     In Formula 5, μ c (Y) and σ c (Y) represent an average and a standard deviation of the c-th FDI. The two values are obtained from the FDI profile created when the FDI value is normal. Z c  represents a normalized value of a degree of difference between an average value of currently observed data and an average value of the FDI profiles in terms of the standard deviation. 
     In operation  560 , the FDI level calculating unit  230  of the fault management device  200  of the smart device calculates the specific c-th FDI level using the value Z c  as in the following Formula 6.
 
 F   lev ( f   c )= F   CDF ( K+Z   c )  (6)
 
     In Formula 6, F lev (f c ) represents a level function to evaluate a specific c-th FDI(f c ) level. This level function may have a form of a cumulative distribution function (CDF) of a specific probability distribution, that is, F CDF (.). Here, K represents an arbitrary constant value and it may be adjusted by the operator. Moreover, Formula 6 defines a level function that may be used when an increase of the FDI value can be determined as the fault generation. However, even when the fault is generated due to a decrease of the FDI value or a deviation from a specific value, the above process may be easily adapted. 
     In operation  570 , the FDI level calculating unit  230  of the fault management device  200  of the smart device determines whether an absolute value of the c-th FDI level is 0. This is to determine whether the fault of the smart device is generated. 
     When the determination result of operation  570  is that the absolute value of the FDI level is not 0, that is, it is determined that the fault is generated in the smart device, the FDI level calculating unit  230  of the fault management device  200  of the smart device requests an analysis process in operation  580 . However, when the determination result of operation  570  is that the absolute value of the FDI level is 0, that is, it is determined that the fault is not generated in the smart device, the FDI level calculating unit  230  of the fault management device  200  of the smart device proceeds to operation  520 . 
     The fault detection operation based on the FDI level explained in  FIG. 5  may be repeatedly performed with respect to the plurality of FDIs in the same way. 
       FIGS. 6A and 6B  are flowcharts for describing fault analysis operations according to the embodiment of the invention. 
     In operation  610 , the fault analysis process maintains a standby state until a request is received. 
     The analysis request is received in operation  620 , and in operation  630  the repair listing unit  240  of the fault management device  200  of the smart device obtains all repairs including the specific DO (for example, an x-th DO: DO x ) by retrieving them from the repair DB  290  and sorts them based on specific criteria (for example, date of repair generation). 
     In operation  640 , the association degree checking unit  250  of the fault management to device  200  of the smart device calculates a relative occurrence frequency of the specific c-th FDI in repairs including the specific DO x  using the following Formula 7 and Formula 8. That is, it calculates how many occurrence frequencies the individual FDI has with respect to all repairs including the specific DO. 
     
       
         
           
             
               
                 
                   
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                   ( 
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     In Formula 7, rf(.) represents a function indicating a relative occurrence frequency, f c  represents a c-th individual FDI, and DO x  represents an x-th DO. An individual repair including the DO x  also includes a subset (f j   sub ) of a different FDI. That is, the repair is configured with a pair of a DO x  and an f j   sub . Further, |R(DO x )| represents a number of all repairs (cardinality) including the DO x . 
     
       
         
           
             
               
                 
                   
                     ind 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       ( 
                       
                         
                           f 
                           c 
                         
                         , 
                         
                           f 
                           sub 
                           j 
                         
                       
                       ) 
                     
                   
                   = 
                   
                     { 
                     
                       
                         
                           
                             1 
                             , 
                           
                         
                         
                           if 
                         
                         
                           
                             
                               f 
                               c 
                             
                             ∈ 
                             
                               f 
                               sub 
                               j 
                             
                           
                         
                       
                       
                         
                           
                             0 
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                           if 
                         
                         
                           
                             
                               f 
                               c 
                             
                             ∉ 
                             
                               f 
                               sub 
                               j 
                             
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   8 
                   ) 
                 
               
             
           
         
       
     
     In Formula 8, ind(f c , f j   sub ) serves as an indicator that returns 1 when f c  is an element of the f j   sub  and returns 0 or not. Therefore, rf (f c , DO x ) counts a case in which the specific FDI is the element of the f j   sub  in all repairs including the DO x  and the counted value is divided by a number of all repairs. As a result, a relative frequency at which the f c  occurs in the repair associated with the DO x  may be calculated. 
     In operation  660 , the association degree checking unit  250  calculates an association degree between the DO x  and the f c  using following Formula 9.
 
 A   deg ( rf ( f   c ,DO x )):[0,1]→[0,1]  (9)
 
     In Formula 9, Adeg(.) represents a function that quantitatively indicates an association degree between the DO x  and the f c . The task of checking a relative occurrence frequency is called association degree checking. The association degree checking will be described in detail below with reference to  FIG. 7 . 
     In operation  670 , the relevance estimating unit  260  calculates a relevance estimation value between the DO x  and the f c  using Formula 10 and Formula 11 given below. 
     The following Formula 10 and Formula 11 calculate a degree of relevance of the specific DO as a major cause and a countermeasure for the individual FDI and are example formulas necessary to sum the individual relevance of how great a degree of relevance the same DO has with respect to all FDIs.
 
 re ( f   c )= A   deg ( rf ( f   c   ,DO   x ))· F   dis ( f   c   ,DO   x )  (10)
 
     In Formula 10, re(f c ) represents a relevance estimation function and this function is defined as a product of an association function A deg (rf(f c , DO T )) and a distance function Formula 11 described below. That is, the A deg (.) function serves as a weight factor in Formula 10. 
     
       
         
           
             
               
                 
                   
                     
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     In Formula 11, F di (f c , DO T ) is a distance estimation function designed to differently estimate a deviation between f c  and f c  of the profile according to the relative occurrence frequency. For example, when the relative occurrence frequency of the f c  is equal to or greater than 0.5, the level function F lev (f c ) is a distance value, and when the relative occurrence frequency of the f c  is less than 0.5, 1−F lev (f c ) is the distance value. 
     As illustrated in  FIG. 6B , in operation  680 , the relevance estimating unit  260  determines whether the f c  is the last one. 
     When the determination result of operation  680  is that the f c  is not the last one, the relevance estimating unit  260  selects a next FDI (for example: f c +1) in operation  690 , and proceeds to operation  650 . 
     On the other hand, when the determination result of operation  680  is that the f c  is the last one, the relevance estimating unit  260  sums and stores relevance values of all FDIs with respect to the specific DO using following Formula 12 in operation  700 .
 
 S ( DO   x )=Σ ∀f     c     εFDI   re ( f   c )  (12)
 
     Formula 12 calculates all FDIs levels and a relative occurrence frequency of each FDI with respect to the specific individual DO x . The association degree between the f c  and the individual DO x  is measured using the calculated value as a weight factor. After the relevance degree between the f c  and the individual DO x  is calculated, relevance values of all FDIs with respect to the specific DO x  are summed, and thereby the relevance of the FDI with respect to the DO x  may be quantitatively evaluated. 
     In operation  710 , the relevance estimating unit  260  determines whether the DO x  is the last one. 
     When the determination result of operation  710  is that the DO x  is not the last one, the relevance estimating unit  260  selects a next DO (for example, DO x +1) in operation  690 , and proceeds to operation  640 . 
     On the other hand, when the determination result of operation  710  is that the DO x  is the last one, the relevance estimating unit  260  compares relevance values of FDI summed with respect to a different last DO in operation  730 . 
     In operation  740 , the relevance estimating unit  260  determines the specific DO having the highest relevance value as an optimum repair of the smart device  100  in which the value of a corresponding FDI is observed. That is, the DO x  having the highest quantitative value is finally given as a repair for the FDI. 
       FIG. 7  is a graph for describing association degree checking according to the embodiment of the invention. 
     As illustrated in  FIG. 7 , when an occurrence frequency of the f c  increases (that is, a value of rf(f c , DO x ) is greater than 0.5 or close to 1) or the occurrence frequency is significantly low (that is, a value of rf(f c , DO x ) is less than 0.5 or close to 0), an association value Adeg(rf(f c , DO x )) is close to 1, and thereby a high association degree between the f c  and the DO x  is represented. However, when a value of rf(f c , DO x ) is 0.5, since the f c  is included in 50% of the repair and not included in the remaining 50% of the repair, it is difficult to clearly determine the association. Accordingly, in this case, the association value is represented as 0 and no association is assumed. 
     For this purpose, using the graph (y=4*(x−0.5)^2, x=rf(f c , DO x )) illustrated in  FIG. 7 , the following association is quantified. The graph illustrated in  FIG. 7  is only an example to calculate a degree of association between the DO x  and the f c  and the operator may select and appropriately use a different form of graph according to characteristics of the FDI. 
     According to the embodiments of the invention, since the fault of the personal terminal including the smart device is automatically matched with the FDI and the DO in real time, it is possible to accurately detect the fault and determine corresponding cause. 
     Moreover, since a method which not only determines whether or not there is a fault as in conventional methods, but in which the FDI indicating fault detection and its corresponding most appropriate fault cause (root cause(s)) and countermeasure (action(s)) are matched as a pair is provided, practical measure (repair) of the terminal fault may be possible. Conventional fault detection systems based on thresholds have limitations, for example, accuracy of fault detection is greatly influenced by an arbitrarily set threshold, a preset threshold needs to be repeatedly recalibrated according to the case confirmed as the fault, or different thresholds need to be preset according to target performance of the fault, events, and environments of the smart device. However, the invention may perform fault detection and analysis without depending on a threshold. 
     The invention provides FDI indicators considering a variety of performance indicators or capable of adding or deleting a variety of performance indicators, and a variety of profiles according to a measured time zone, a target smart device, a target application, and a target event may be easily generated using only three pieces of data including an average value, a variance value, and a type of the probability distribution model. As a result, it is possible to generate and manage massive profiles. Due to generation and management of the massive profiles, fault detection and analysis may be sensitively performed according to changes of a size and a state of the network including the smart device. 
     Furthermore, the invention has an advantage in that the level function of determining whether an initial fault is generated, the function of calculating an association degree between the specific FDI and the DO, and the function of estimating relevance between the specific FDI and the DO may be easily implemented, and computational complexity of corresponding functions is also low. 
     In addition, the invention has a high effectiveness since it provides a structural characteristic that can be applied without modification of an existing system (legacy system). In such a situation, the terminal fault causes cost burdens according to offline follow-up actions in a corporation and damage to a corporation&#39;s image. However, the invention may automatically process such problems in real time with a high accuracy. 
     The above-described descriptions are only exemplary of the invention. It will be understood by those skilled in the art that modifications in form may be made without departing from the spirit and scope of the invention. Therefore, the invention is not limited to the above-described embodiments and encompasses all modifications and equivalents that fall within the scope of the appended claims. 
     The present invention can be implemented as computer-readable code in a computer-readable recording medium. The computer-readable recording medium includes all types of recording media in which computer-readable data is stored. Examples of the computer-readable recording medium include a ROM, a RAM, a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage. Further, the recording medium may be implemented in the form of carrier waves, such as those used in Internet transmission. In addition, the computer-readable recording medium may be distributed among computer systems over a network such that computer-readable codes may be stored and executed in a distributed manner. 
     A number of examples have been described above. Nevertheless, it will be understood that various modifications may be made. For example, suitable results may be achieved if the described techniques are performed in a different order and/or if components in a described system, architecture, device, or circuit are combined in a different manner and/or replaced or supplemented by other components or their equivalents. Accordingly, other implementations are within the scope of the following claims.