In recent years social media data analytics allow enterprises to adopt a data driven approach to manage their processes. Although social media provide a plethora of data, still much work needs to be done to transform these data into services that businesses can use and make insightful decisions. In this work, we address one of the most important problems in business, the relationship with the customers and more precisely the identification of loyal customers. We use behavioral analytics to model and process customer actions. We extract user behavior based on our unified crawling approach and collect data from three different social media namely Reddit, Twitter and YouTube. We are extracting for each user three different behaviors namely communication, sentiment and product mix and convert them into a 3-D time-series. We use shapelet clustering to determine the loyal users. To verify our approach, we develop a set of metrics based on trust, commitment and engagement and we show that our approach results in differentiating the loyal users successfully. Moreover, we validate our results presenting a word cloud visualization. We extend our methodology introducing a semantic data transformation algorithm where we use the topic extraction, reducing the time-series to a more relevant one. Our experiments show that based on the verification metrics, our transformation increases the accuracy of the clustering results.

Human decision making is central in many functions across a broad spectrum of fields such as marketing, investment, smart contract formulations, political campaigns, and organizational strategic management. Behavioral economics seeks to study the psychological, cultural, and social factors contributing to decision making along reasoning. It should be highlighted here that behavioral economics do not negate classical economic theory but rather extend it in two distinct directions. First, a finer granularity can be obtained by studying the decision making process not of massive populations but instead of individuals and groups with signal estimation or deep learning techniques based on a wide array of attributes ranging from social media posts to physiological signs. Second, time becomes a critical parameter and changes to the disposition towards alternative decisions can be tracked with input-output or state space models. The primary findings so far are concepts like bounded rationality and perceived risk, while results include optimal strategies for various levels of information awareness and action strategies based on perceived loss aversion principles. From the above it follows that behavioral economics relies on deep learning, signal processing, control theory, social media analysis, affective computing, natural language processing, and gamification to name only a few fields. Therefore, it is directly tied to computer science in many ways. THECOG will be a central meeting point for researchers of various backgrounds in order to generate new interdisciplinary and groundbreaking results.

At the dawn of the Internet era graph analytics play an important role in high- and low-level network policymaking across a wide array of fields so diverse as transportation network design, supply chain engineering and logistics, social media analysis, and computer communication networks, to name just a few. This can be attributed not only to the size of the original graph but also to the nature of the problem parameters. For instance, algorithmic solutions depend heavily on the approximation criterion selection. Moreover, iterative or heuristic solutions are often sought as it is a high dimensional problem given the high number of vertices and edges involved as well as their complex interaction. Replacing under constraints a directed graph with an undirected one having the same vertex set is often sought in applications such as data visualization, community structure discovery, and connection-based vertex centrality metrics. Polar decomposition is a key matrix factorization which represents a matrix as a product of a symmetric positive (semi)definite factor and an orthogonal one. The former can be an undirected approximation of the original adjacency matrix. The proposed graph approximation has been tested with three Twitter graphs with encouraging results with respect to density, Fiedler number, and certain vertex centrality metrics based on matrix power series. The dataset was hosted in an online MongoDB instance.

Human decision making is central in many functions across a broad spectrum of fields such as marketing, investment, smart contract formulations, political campaigns, and organizational strategic management. Behavioral economics seeks to study the psychological, cultural, and social factors contributing to decision making along reasoning. It should be highlighted here that behavioral economics do not negate classical economic theory but rather extend it in two distinct directions. First, a finer granularity can be obtained by studying the decision making process not of massive populations but instead of individuals and groups with signal estimation or deep learning techniques based on a wide array of attributes ranging from social media posts to physiological signs. Second, time becomes a critical parameter and changes to the disposition towards alternative decisions can be tracked with input-output or state space models. The primary findings so far are concepts like bounded rationality and perceived risk, while results include optimal strategies for various levels of information awareness and action strategies based on perceived loss aversion principles. From the above it follows that behavioral economics relies on deep learning, signal processing, control theory, social media analysis, affective computing, natural language processing, and gamification to name only a few fields. Therefore, it is directly tied to computer science in many ways. THECOG will be a central meeting point for researchers of various backgrounds in order to generate new interdisciplinary and groundbreaking results.

In recent years, machine learning has been used for data processing and analysis, providing insights to businesses and policymakers. Deep learning technology is promising to further revolutionize this processing leading to better and more accurate results. Current trends in information and communication technology are accelerating widespread use of web services in supporting a service-oriented architecture (SOA) consisting of services, their compositions, interactions, and management. Deep learning approaches can be applied to support the development of SOA-based solutions, leveraging the vast amount of data on web services currently available. On the other hand, SOA has mechanisms that can support the development of distributed, flexible, and reusable infrastructures for the use of deep learning. This paper presents a literature survey and discusses how SOA can be enabled by as well as facilitate the use of deep learning approaches in different types of environments for different levels of users.

Startups arguably contribute to the current business landscape by developing innovative products and services. The discovery of business partners and employees with a specific background which can be verified stands out repeatedly as a prime obstacle. LinkedIn is a popular platform where professional milestones, endorsements, recommendations, and skills are posted. A graph search algorithm with a BFS and a DFS strategy for seeking trusted candidates in LinkedIn is proposed. Both strategies rely on a metric for assessing the trustworthiness of an account according to LinkedIn attributes. Also, a stochastic vertex selection mechanism reminiscent of preferential attachment guides search. Both strategies were verified against a large segment of the vivid startup ecosystem of Patras, Hellas. A higher order probabilistic analysis suggests that BFS is more suitable. Findings also imply that emphasis should be given to local networking events, peer interaction, and to tasks allowing verifiable credit for the respective work.

The identification of social media communities has recently been of major concern, since users participating in such communities can contribute to viral marketing campaigns. In this work, we focus on users' communication considering personality as a key characteristic for identifying communicative networks i.e., networks with high information flows. We describe the Twitter Personality based Communicative Communities Extraction (T-PCCE) system that identifies the most communicative communities in a Twitter network graph considering users' personality. We then expand existing approaches in users' personality extraction by aggregating data that represent several aspects of user behavior using machine learning techniques. We use an existing modularity based community detection algorithm and we extend it by inserting a post-processing step that eliminates graph edges based on users' personality. The effectiveness of our approach is demonstrated by sampling the Twitter graph and comparing the communication strength of the extracted communities with and without considering the personality factor. We define several metrics to count the strength of communication within each community. Our algorithmic framework and the subsequent implementation employ the cloud infrastructure and use the MapReduce Programming Environment. Our results show that the T-PCCE system creates the most communicative communities.

The Grid environment is rapidly emerging as a collaborative environment for a wide range of applications. Applications that demand intense problem solving capabilities, extensive data sharing and processing, collaborative relationships among distributed participants employ grid computing. Grid environments provide efficient and scalable access to distributed computing resources among geographically distributed participants. In a grid environment virtual organizations are formulated and managed from a computing resource point of view. The grid provider allows for the dynamic discovery of computing resources, the immediate allocation and provision of the resources and the management and provision of secure access. In our work we examine the legal implications that arise in such environments. Although the security problem in grid environment is being addressed from the technological point of view, there is no work to identify the legal issues that are arising in collaborative environments.

Efficient and effective management is a key to business success. In particular, process management technologies can help smoothen and enhance the production. In this paper, we propose a sophisticated alert management system (AMS) for workflow management in particular for concrete batching plant. For concrete batching plant, there are challenges in monitoring schedules and handling their changes. An AMS can help monitor the whole production process and keep track of any exceptions by sending alerts and receiving responses in order to assist in resources planning and production scheduling of relevant stakeholders. We develop a model for specifying alerts and outlining the mechanism of the AMS. The applicability of the AMS for workflow integration, data integration, and exception handling is also examined by studying the stakeholders ' requirements.

Recent advances in mobile technologies and infrastructures have led to increasing demands for ubiquitous access to enterprise services from mobile handheld devices, such as mobile phones and PDAs. The context in which such a service is used becomes an integral part of the associated enterprise application. This demands a new paradigm for system requirements elicitation and design in order to make good use of such extended context information. Instead of redesigning or adapting existing enterprise services in an ad-hoc manner, we introduce a methodology for the elicitation of context-aware adaptation requirements and the matching of context-awareness features to the target context by capability matching. For the implementation of these adaptations, we propose the use of three tiers of views: user interface views, data views, and process views. This approach centers on a novel notion of process views to service adaptation according to their context. We demonstrate our methodology by extending an enterprise appointment service into a ubiquitous one with context support

New technologies have introduced new ways in business transactions where online contracting is complementing and even substituting traditional paper-based transactions. One of the major recent innovations of online contracting is the use of intelligent agents to make contracts among users and businesses around the globe. Despite recent legislations on electronic contracting, there are no legislations governing automatic agent transactions except one preliminary attempt in the USA. We identify the key problem rooted at the authorization management in agent delegation as well as the proper legal identity of agents. Therefore, we advocate solutions that consider both legal and technical aspects. Based on current legal and business practices, we develop a conceptual model for agent authorization and identity management We propose an Agent Authorization Platform (AAP) for the enforcement of agent authorization during contract establishment as well as the maintenance of the legal identity of agents. The AAP also supports alerts and acknowledgment to further enforce the user's manifestation of assent to the contract terms. We also detail a required security scheme for the AAP based on Public Key Infrastructure (PKI) technologies to demonstrate the feasibility of our approach.

With the recent adoption of marketing activities outsourcing, there have been increasing demands and concerns for privacy control. The traditional approach of a bulk transmission of the customers' information to a marketing company cannot meet such demands, especially in the finance and healthcare businesses. Therefore, we propose a layered architecture and a development methodology for end-to-end privacy control over the export of each individual customer's records through a Web services platform, according to the corresponding enterprise's privacy control policies. A Web services system, with up-dated security and privacy facilities, can provide a suitable interoperation platform for required application-to-application interactions over the Internet. We further develop a conceptual model and an interaction protocol to send only the required part of a customer's records at a time. We illustrate our approach for end-to-end privacy control with a tele-marketing case study and show how the software of the outsourced call center can be integrated effectively with the Web services of a bank to protect privacy. Copyright 2005 ACM.

Recent advances in mobile technologies have greatly extended traditional communication technologies to mobile devices. At the same time, healthcare environments are by nature "mobile" where doctors and nurses do not have fixed workspaces. Irregular and exceptional events are generated in daily hospital routines, such as operations rescheduling, laboratory/examination results, and adverse drug events. These events may create requests that should be delivered to the appropriate person at the appropriate time. Those requests that are classified as urgent are referred to as alerts. Efficient routing and monitoring of alerts are keys to quality and cost-effective healthcare services. Presently, these are generally handled in an ad hoc manner. In this paper, we propose the use of a healthcare alert management system to handle these alert messages systematically. We develop a model for specifying alerts that are associated with medical tasks and a set of parameters for their routing. We design an alert monitor that matches medical staff and their mobile devices to receive alerts, based on the requirements of these alerts. We also propose a mechanism to handle and reroute, if necessary, an alert message when it has not been acknowledged within a specific deadline.

With recent advances in mobile technologies and infrastructures, there are increasing demands for ubiquitous access to networked services. These services, generally known as m-services, extend supports from Web browsers on personal computers to handheld devices, such as mobile phones and PDAs. However, in general, the capabilities and bandwidth of these devices are significantly inferior to desktop computers over wired connections, which have been assumed by most Internet services. Instead of redesigning or adapting m-services in an ad-hoc manner for multiple platforms available in handheld devices, we propose a methodology for such adaptation based on three tiers: user interface views, data views, and process views. These views provide customization and help balance security and trust. User interface views provide alternative presentations of inputs and outputs. Data views summarize data over limited bandwidth and map heterogeneous data sources. In addition, we introduce a novel approach of applying process views to m-service adaptation, where mobile users may execute a more concise version or modified procedures of the original process. The process view also serves as the key mechanism for integrating user interface views and data views. In addition, we present a formal model on view consistency and integrity in our methodology. We demonstrate the feasibility of our methodology by extending a service negotiation subsystem into an m-service with multi-platform support.