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Another interesting work done in time series (Keogh, Lonardi et al. 2002) proposed an algorithm that detects surprising patterns in a time series database in linear space and time. This algorithm is named TARZAN. The definition of surprising in this algorithm is general and domain independent, describing a pattern as surprising if the frequency with which we encounter it differs greatly from that expected given previous experience. 3 PROBLEM MINING THE RELATIONSHIPS IN THE FORM OF THE PREDISPOSING FACTORS We get an OSS data set from http://sourceforge.net which is the world’s largest Open Source software development website. There are 1,097,341 records, 41,540 projects in this data set. This data set consists of seventeen attributes include time attribute. The time attribute of each record in this data set is monthly. Each project in this data set is a software. There are many attributes that show various activities. We are interested in thirteen attributes which indicate the number of activities in this data set. The data of these thirteen attributes are all numeric. The value of the Download attribute is the number of the downloads. So the Download attribute is the indicator showing how popular the software is and show how successful the development of the software is. We are interested in the significant change of the number of the Download attribute. Then we employ the idea of the event detection technique proposed by (Guralnik and Srivastava 1999) to detect the event of the Download attribute. The event of our interest is the direction of the significant change of the data which can be up or down. We want to find the predisposing factor and the co-incident factor of the Download event. We employ the same idea about the reference event as proposed in (Bettini, Wang et al. 1998) which is the fixed event of interest and we want to find the other events related to the reference event. So we call the Download attribute as the reference attribute and call the event of the Download attribute as the reference event. The predisposing factor of the reference event can possibly be the cause of the reference event or the cause of the other event which is the cause of the reference event. And the co-incident factor of the reference event can possibly be the effect of the reference event or the effect of the other event which is the effect of the reference event somehow or be the event happening at the same time as the reference event happens or can be the result from the same cause of the reference event or just be the result from the other event which happens at the same time of the reference event happens. To make this concept clear, see the example as follow J K A C H I L B G D F E time 137 Figure1: The relationships among the events over time If we have the event A, B, C, D, E, F, G, H, I, J, K, L and the relationships among them as shown in Figure 1. That is H and I give B; A and B give C; D and E give F; C and F give G; J and C give K; K and G give L. But in our data set consists of only A, C, F, G, H, L. And the reference event is C. We can see that H and A happen before C, we may say that A is the cause of C and/or H is the cause of C. But in the real relationship as shown above, we know that H is not the cause of C directly or it is not because A and H give C. So we call A and H are the predisposing factors of C. And we find that F happens at the same time as C happens. And G and L happen after C. We call F as the co-incident factor of C. We can see from the relationship that G is the result from C and F. L is the result from G which is the result from C. And F is the co-factor of C that gives G. Only G is the result from C directly. L is the result from G which is the result from C. We want to find the exact relationships among these events. Unfortunately, no one can guarantee that our data set of consideration consists of all of the related factors or events. We can see from the diagram or the relationship shown in the example that the relationship among the events can be complex. And if we don’t have all of the related events, we cannot find all of the real relationships. So what we can do with the possible incomplete data set is mining the predisposing factor and co-incident factor of the reference event. Then the users can further consider these factors and collect more data which related to the reference event and explore more in depth by themselves on the expert ways in their specific fields. The main idea in this part is the predisposing factor can possibly be the cause of the reference event and the co-incident factor can possible be the effect of the reference event. So we employ the same idea as proposed in (Blum 1982; Blum 1982) that the cause happens before the effect. The effect happens after the cause. We call the time point when the reference event happens as the current time point. We call the time point before the current time point as the previous time point. And we call the time point after
138 ENTERPRISE INFORMATION SYSTEMS VI the current time point as the post time point. Then we define the predisposing factor of the reference event as the event which happens at the previous time point. And we define the co-incident factor of the reference event as the event happens at the current time point and/or the post time point. 4 BASIC DEFINITIONS AND FRAMEWORK The method to interpret the result is selecting the large fraction of the positive slope and the negative slope at each time point. If it is at the previous time point that means it is the predisposing factor. If it is at the current time point and/or the post time point that means it is the co-incident factor. Definition1: A time series data set is a set of records r such that each record contains a set of attributes and a time attribute. The value of time attribute is the point of time on time scale such as month, year. rj = { a1, a2, a3, …, am, tj } where rj is the j th record in data set Definition 2: There are two types of the attribute in time series data set. Attribute that depends on time is dynamic attribute (�), other wise, it is static attribute (S). Definition 3: Time point (ti) is the time point on time scale. Definition 4: Time interval is the range of time between two time points [t1, t2]. We may refer to the end time point of interval (t2 ). Definition 5: An attribute function is a function of time whose elements are extracted from the value of attribute i in the records, and is denoted as a function in time, ai(tx) ai(tx) = ai � rj where ai attribute i; tx time stamp associated with this record Definition 6: A feature is defined on a time interval [t1 ,t2], if some attribute function ai(t) can be approximated to another function � (t) in time , for example, ai(t) � � (t) , �t � [t1 ,t2] We say that � and its parameters are features of ai(t) in that interval [t1 ,t2]. If �(t) = � i t + � i in some intervals, we can say that in the interval, the function ai(t) has a slope of � i where slope is a feature extracted from ai(t) in that interval Definition 7: Slope (� i) is the change of value of a dynamic attribute (ai) between two adjacent time points. where � i = ( ai( tx) - ai( tx-1) ) / tx - tx-1 ai( tx) is the value of ai at the time point tx ai (tx-1 )is the value of ai at the time point tx-1 Definition 8: Slope direction d(�i ) is the direction of slope. If � i > 0, we say d� = 1 If � i < 0, we say d� = -1 If � i � 0, we say d� = 0 Definition 9: A significant slope threshold (�I) is the significant slope level specified by user. Definition 10: Reference attribute (at ) is the attribute of interest. We want to find the relationship between the reference attribute and the other dynamic attributes in the data set. Definition 11: An event (E1) is detected if �i ��I Definition 12: Current time point (tc) is the time point at which reference variable’s event is detected. Definition 13: Previous time point (tc-1) is the previous adjacent time point of tc Definition 14: Post time point (tc+1) is the post adjacent time point of tc Proposition 1: Predisposing factor of at denoted as PE1at is an ordered pair (ai , d� ) when ai � � np If ai tc-1 > nn ai tc-1 , then PE1at �(ai , 1) np If ai tc-1 < nn ai tc-1 , then PE1at �(ai , -1) where np ai tc-1 is the number of positive slope of E1 of ai at tc-1 nn ai tc-1 is the number of negative slope of E1of ai at tc-1 Proposition 2: Co-incident factor of at denoted as CE1at is an ordered pair (ai , d� ) when ai �� If (( np ai tc > nn ai tc ) v ( np ai tc+1 > nn ai tc+1 )) , then CE1at �(ai , 1) If (( np ai tc < nn ai tc ) v ( np ai tc+1 < nn ai tc+1 )) , then CE1at �(ai , -1) where np ai tc is the number of positive slope of E1 of ai at tc nn ai tc is the number of negative slope of E1 of ai at tc np ai tc+1 is the number of positive slope of E1 of ai at tc+1 nn ai tc+1 is the number of negative slope of E1 of ai at tc+1 5 ALGORITHM Now we present a new algorithm. Input: The data set which consists of numerical dynamic attributes. Sort this data set in ascending order by time, at, �I Output: np ai tc-1 , nn ai tc-1 , np ai tc , nn ai tc , np ai tc+1 , nn ai tc+1 , PE1at , CE1at
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vi TABLE OF CONTENTS PART 1 - DATAB
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viii TABLE OF CONTENTS A WIRELESS A
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CONFERENCE COMMITTEE Honorary Presi
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Hung, P. (AUSTRALIA) Jahankhani, H.
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Invited Papers
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2 ENTERPRISE INFORMATION SYSTEMS VI
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10 ENTERPRISE INFORMATION SYSTEMS V
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EVOLUTIONARY PROJECT MANAGEMENT: MU
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MANAGING COMPLEXITY OF ENTERPRISE I
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ASSESSING EFFORT PREDICTION MODELS
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ORGANIZATIONAL AND TECHNOLOGICAL CR
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ASAP Processes W Project Kickoff A
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since it means changing the relevan
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ACME-DB: AN ADAPTIVE CACHING MECHAN
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ACME-DB: AN ADAPTIVE CACHING MECHAN
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Hit Rate (%) ACME-DB: AN ADAPTIVE C
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5.3.6 Effect on all Policies by Int
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ACKNOWLEDGEMENTS We would like to t
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This paper describes the data sampl
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Page 119 and 120: PART 2 Artificial Intelligence and
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Page 169 and 170: AN EXPERIENCE IN MANAGEMENT OF IMPR
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A POLYMORPHIC CONTE XT FRAME TO SUP
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A POLYMORPHIC CONTE XT FRAME TO SUP
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FEATURE MATCHING IN MODEL-BASED SOF
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combination of solution domains - a
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• features for all the concepts:
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Existence In both steps it is possi
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matching strategies are maximal, mi
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TOWARDS A META MODEL FOR DESCRIBING
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elementary message (ae-message) can
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problems on a pragmatic level, whic
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TOWARDS A META MODEL FOR DESCRIBING
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INTRUSION DETECTION SYSTEMS USING A
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INTRUSION DETECTION SYSTEMS USING A
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(k� 1) (k) (k�1) (k) p � w
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Table 5: Performance Comparison of
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A USER-CENTERED METHODOLOGY TO GENE
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carries out the second phase of the
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1 1 1 1 2 PROCESS STORE ENTITY EDGE
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constraints rules. KOGGE (Ebert et
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PART 4 Software Agents and Internet
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230 ENTERPRISE INFORMATION SYSTEMS
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CABA 2 L A BLISS PREDICTIVE COMPOSI
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y disables evidencing severe mental
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Figure 4: Symbols emission in DAR-H
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they evidence a generalization erro
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A METHODOLOGY FOR INTERFACE DESIGN
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and mobility loss coupled with redu
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Tradeoff: The default input is poss
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Sessions on the VABS which are held
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A CONTACT RECOMMENDER SYSTEM FOR A
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A CONTACT RECOMMENDER SYSTEM FOR A
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- "Going to the sources". The user
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Here are the matrix W and P for our
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EMOTION SYNTHESIS IN VIRTUAL ENVIRO
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theme turns out to be surprisingly
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6 FROM FEATURES TO SYMBOLS 6.1 Face
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sions are described by different em
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Electronic Imaging 2000 Conference
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to the accessibility problem for th
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Figure 3: Hierarchical View of the
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4 CONCLUSIONS AND FUTURE WORK On th
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PERSONALISED RESOURCE DISCOVERY SEA
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profile, the user defines his curre
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Abdelkafi, N. .....................
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