系统工程与电子技术
繫統工程與電子技術
계통공정여전자기술
SYSTEMS ENGINEERING AND ELECTRONICS
2015年
4期
949-957
,共9页
刘健%刘利钊%汪建均%顾晓光
劉健%劉利釗%汪建均%顧曉光
류건%류리쇠%왕건균%고효광
邻接矩阵%过程挖掘%预测%商业智能
鄰接矩陣%過程挖掘%預測%商業智能
린접구진%과정알굴%예측%상업지능
adjacency matrix%process mining (PROM)%prediction%business intelligence
将事件日志中蕴含的过程模型看成两紧邻活动的组合,提出两种新的过程模型。首先,利用日志信息中的活动紧邻关系构造邻接矩阵提取过程模型,该模型中每个活动仅发生一次;其次,为避免过程模型中出现回路或者环路而造成模型预测精度降低的情况发生,在构造的邻接矩阵中增加活动在事件日志中所处的顺序位次,构造含有活动位次信息的邻接矩阵,以此为基础上进一步提取过程模型,该模型中每个活动在同一个位次上仅发生一次;再次,通过矩阵中的信息可获得过程模型中每个上层节点到各个下层节点的路径与相应概率;接下来,根据事件日志中信息的类型和特征,利用过程模型对决策者所需要的信息(如活动名称、等待时间、发生概率)进行预测;最后,利用随机数据与实际数据同基于序列提取规则的过程模型预测结果进行比较,验证所提模型的实际有效性。
將事件日誌中蘊含的過程模型看成兩緊鄰活動的組閤,提齣兩種新的過程模型。首先,利用日誌信息中的活動緊鄰關繫構造鄰接矩陣提取過程模型,該模型中每箇活動僅髮生一次;其次,為避免過程模型中齣現迴路或者環路而造成模型預測精度降低的情況髮生,在構造的鄰接矩陣中增加活動在事件日誌中所處的順序位次,構造含有活動位次信息的鄰接矩陣,以此為基礎上進一步提取過程模型,該模型中每箇活動在同一箇位次上僅髮生一次;再次,通過矩陣中的信息可穫得過程模型中每箇上層節點到各箇下層節點的路徑與相應概率;接下來,根據事件日誌中信息的類型和特徵,利用過程模型對決策者所需要的信息(如活動名稱、等待時間、髮生概率)進行預測;最後,利用隨機數據與實際數據同基于序列提取規則的過程模型預測結果進行比較,驗證所提模型的實際有效性。
장사건일지중온함적과정모형간성량긴린활동적조합,제출량충신적과정모형。수선,이용일지신식중적활동긴린관계구조린접구진제취과정모형,해모형중매개활동부발생일차;기차,위피면과정모형중출현회로혹자배로이조성모형예측정도강저적정황발생,재구조적린접구진중증가활동재사건일지중소처적순서위차,구조함유활동위차신식적린접구진,이차위기출상진일보제취과정모형,해모형중매개활동재동일개위차상부발생일차;재차,통과구진중적신식가획득과정모형중매개상층절점도각개하층절점적로경여상응개솔;접하래,근거사건일지중신식적류형화특정,이용과정모형대결책자소수요적신식(여활동명칭、등대시간、발생개솔)진행예측;최후,이용수궤수거여실제수거동기우서렬제취규칙적과정모형예측결과진행비교,험증소제모형적실제유효성。
Viewing the process model in event logs as the combination of the two adjacent activities,two no-vel process models are proposed.First,the process model is extracted by constructing adjacency matrix,taking advantage of the adjacency relationships of activities in the event logs.To improve the prediction accuracy of the model,loops are avoided in the process model.So,each activity in this model will only happen once.Second, the serial number of activities in the event logs to the adjacency matrix is added,constructing a new adjacency matrix with sequence information.Based on the new adjacency matrix,the process model is extracted.Each ac-tivity in this model will only happen once at the same sequence position.Third,with the adjacency matrix,the path from each prior node to next nodes in the process model and their corresponding probabilities are gotten. Then,according to the type and characteristic information of the event logs,predictions of the information are made which are needed by decision-makers,e.g.activity name,waiting time,and probability based on process model.Finally,the effectiveness of the proposed models by comparing the prediction results of random data and real data based on process models is verified.