By Nitin Agarwal, Shamanth Kumar, Huiji Gao, Reza Zafarani, Huan Liu (auth.), Longbing Cao, Philip S. Yu (eds.)
'Behavior' is an more and more vital inspiration within the medical, societal, monetary, cultural, political, army, residing and digital worlds. habit computing, or habit informatics, comprises methodologies, options and functional instruments for interpreting and analyzing behaviours in those a number of worlds. habit computing contributes to the in-depth knowing, discovery, purposes and administration of habit intelligence. With contributions from best researchers during this rising box habit Computing: Modeling, research, Mining and selection contains chapters on: illustration and modeling behaviors; habit ontology; behaviour research; behaviour trend mining; clustering advanced behaviors; type of complicated behaviors; behaviour influence research; social behaviour research; organizational behaviour research; and behavior computing purposes. habit Computing: Modeling, research, Mining and choice presents a devoted resource of reference for the speculation and functions of habit informatics and behaviour computing. Researchers, examine scholars and practitioners in habit reports, together with computing device technology, behavioral technological know-how, and social technological know-how groups will locate this cutting-edge quantity invaluable.
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Additional info for Behavior Computing: Modeling, Analysis, Mining and Decision
If the token is not validated, the TTP aborts the transaction and advises the merchant simultaneously. Otherwise, the TTP sends the decrypting key and the payment token, both digitally signed with TTP private key, to the customer and the merchant, respectively. Finally, after a somewhat complex process, the customer will receive the decrypted product if everything is all right, or else the transaction will be aborted by the TTP. In stage-based Fig. 6, we divide the whole graphical model into three parts, namely Preliminary Stage, Main Stage and Final Stage.
State differently, if we aim to discover some frequent patterns, we should give a specific definition of the term “support”, then frequent pattern is an activity sequence with support greater than or equal to a given minimum threshold support, while exceptional behavior is concerned on the condition that both “Intentional Interestingness” and “Exceptional Interestingness” are well defined , what hidden means must be confirmed before mining hidden sequences. In this section, we take advantage of temporal logic to express properties of the desired behavior sequences.
With regard to action sub-model, for the action “Send product”, subject is the merchant, object is the customer, this action occurs at the time when the merchant has accepted the purchase order (PO) and the place is online, the status is in the main stage of online shopping process, it has the constraint of the acceptance of PO. This action has the belief that the customer will buy the product finally, and aims that the product will not be corrupted during transit. To achieve this goal, the merchant draws up a plan to send the copy of the encrypted product together with a signed cryptographic checksum.