In recent years, workflow management systems (WFMSs) have gained popularity both in research as well as in commercial sectors. WFMSs are used to coordinate and streamline business processes of an organization. Often, verylarge WFMSs are used in organizations with users in the range of several thousands and number of process instances in the range of tens of thousands.To simplify the complexity of security administration, it is a common practice in many business organizations to allocate a role to perform each activity in the process and then assign one or more users to each role, and granting an authorization to roles rather than to users. Typically the security policies of the organization are expressed as constraints on users and roles. a well-known constraint is separation of duties. Unfortunately, current role-based access control models are not adequate to model such constraints.To address this issue, in this paper, (1) we present a language to express authorization constraints as clauses in a logic program,(2) provide formal notions of constraint consistency, and (3) propose algorithms to check for the consistency of the constraints and to assign roles and users to the workflow tasks in such a way that no constraints are violated.

In this article, we perform sentiment analyses of Twitter location data. We use two case studies: US presidential elections of 2016 and UK general elections of 2017. For US elections, we plot state-wise user sentiment towards Hillary Clinton and Donald Trump. For UK elections, we download two disparate datasets, using keywords and location coordinates, looking for similar tendencies in sentiment towards political candidates and parties. The overall objective of the two case studies is to evaluate similarity between sentiment of location-based tweets and on-ground public opinion reflected in election results. We discover Twitter location sentiment does indeed corroborate with the election result in both cases. We also discover similar tendencies in Twitter sentiment towards political candidates and parties regardless of the methodology adopted for data collection.

In recent years, clickstream-based Web personalization models for collaborative filtering recommendation have received much attention mainly due to their scalability [10,16,19]. The common personalization models are the Markov model, (sequential) association rule, and clustering. These models have shown strengths and weaknesses in their performance: for instance, the Markov model has higher precision and lower recall than (sequential) association rule and clustering, and vice versa [22]. In order to address the trade-off relationship of precision and recall, some study has combined two or more different models [22] or applied multi-order models [24,27]. The performance increases by these models, however, are at best marginal and still there is room for improving the performance because of their first order (one model type) application in making recommendation. We propose a new hybrid model for improving the performance, especially recall. The proposed hybrid model applies four prediction models -the Markov model, sequential association rule, association rule, and a default model [1,17] -in tandem in their precision order. We evaluated our model with Web usage data, and the result is promising.

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Privacy refers to controlling the dissemination and use of personal data, including information that is knowingly disclosed, as well as data that are unintentionally revealed as a byproduct of the use of information technologies. This paper argues that the high resolution geospatial images of our earth's surface, produced from the earth observing satellites, can make a person visually exposed, resulting in a technological invasion of personal privacy. We propose a suitable authorization model for geospatial data (GSAM) where controlled access can be specified based on the region covered by an image with privilege modes that include view, zoom-in, overlay and identify. We demonstrate how access control can be efficiently enforced using a spatial indexing structure, called MX-RS quadtree, a variant of the MX-CIF quadtree.