Distributed Dynamic Condition Response Structures

We present distributed dynamic condition response structures as a declarative process model in-spired by the workflow language employed by our industrial partner and conservatively generalizing labelled event structures. The model adds to event structures the possibility to 1) finitely specify re-peated, possibly infinite behavior, 2) finitely specify fine-grained acceptance conditions for (possibly infinite) runs based on the notion of responses and 3) distribute events via roles. We give a graph-ical notation inspired by related work by van der Aalst et al and formalize the execution semantics as a labelled transition system. Exploration of the relationship between dynamic condition response structures and traditional models for concurrency, application to more complex scenarios, and further extensions of the model is left to future work.

A Formal Model For Declarative Workflows:Dynamic Condition Response Graphs

C ur re nt bu si ne ss pr oc es st ec hn ol og y is pr et ty go od in su pp or tin g w el l-s tru ct ur ed bu si ne ss pr oc es se s an d ai m at ac hi ev in g a fix ed go al by ca rr yi ng ou ta n ex ac t s

Towards a Set Theoretical Approach to Big Data Analytics

Abstract—Formal methods, models and tools for social big data analytics are largely limited to graph theoretical approaches such as social network analysis (SNA) informed by relational sociology. There are no other unified modeling approaches to social big data that integrate the conceptual, formal and software realms. In this paper, we first present and discuss a theory and conceptual model of social data. Second, we outline a formal model based on set theory and discuss the semantics of the formal model with a real-world social data example from Facebook. Third, we briefly present and discuss the Social Data Analytics Tool (SODATO) that realizes the conceptual model in software and provisions social data analysis based on the conceptual and formal models. Fourth and last, based on the formal model and sentiment analysis of text, we present a method for profiling of artifacts and actors and apply this technique to the data analysis of big social data collected from Facebook page of the fast fashion company, H&M