Chiara Pulice

Efficient Maintenance of All-Pairs Shortest Distances

Computing shortest distances is a central task in many graph applications. Since it is impractical to recompute shortest distances from scratch every time the graph changes, many algorithms have been proposed to incrementally maintain shortest distances after edge deletions or insertions. In this paper, we address the problem of maintaining all-pairs shortest distances in dynamic graphs and propose novel efficient incremental algorithms, working both in main memory and on disk. We prove their correctness and provide complexity analyses. Experimental results on real-world datasets show that current main-memory algorithms become soon impractical, disk-based ones are needed for larger graphs, and our approach significantly outperforms state-of-the-art algorithms.

A Framework Supporting the Analysis of Process Logs Stored in Either Relational or NoSQL DBMSs

The issue of devising efficient and effective solutions for supporting the analysis of process logs has recently received great attention from the research community, as effectively accomplishing any business process management task requires understanding the behavior of the processes. In this paper, we propose a new framework supporting the analysis of process logs, exhibiting two main features: a flexible data model (enabling an exhaustive representation of the facets of the business processes that are typically of interest for the analysis) and a graphical query language, providing a user-friendly tool for easily expressing both selection and aggregate queries over the business processes and the activities they are composed of. The framework can be easily and efficiently implemented by leveraging either “traditional” relational DBMSs or “innovative” NoSQL DBMSs, such as Neo4J.

Community-based delurking in social networks

The participation inequality phenomenon in online social networks between the niche of super contributors and the crowd of silent users, a.k.a. lurkers, has been witnessed in many domains. Within this view, understanding the role that lurkers take in the network is essential to develop innovative strategies to delurk them, i.e., to engage such users into a more active participation in the social network life. In this work, we leverage the boundary spanning theory to enhance our understanding of lurking behaviors, with the goal of improving the task of delurking in social networks. Assuming the availability of a global community structure, we first analyze how lurkers are related to users that take the role of bridges between different communities, unveiling insights into the bridging nature of lurkers and their tendency to acquire information from outside their own community. Moreover, based on a targeted influence maximization method designed for delurking, we also analyze how the learning of users that can best engage lurkers is related to the community structure. We found that the best users to engage lurkers belonging to any particular community, are more often found outside that community, and more specifically they are located in the adjacent communities.

All-pairs shortest distances maintenance in relational DBMSs

Computing shortest distances is a central task in many graph applications. Although many algorithms to solve this problem have been proposed, they are designed to work in the main memory and/or with static graphs, which limits their applicability to many current applications where graphs are subject to frequent updates. In this paper, we propose novel efficient incremental algorithms for maintaining all-pairs shortest distances in dynamic graphs. We experimentally evaluate our approach on real-world datasets, showing that it outperforms current algorithms designed for the same problem.

Discovering User Behavioral Features to Enhance Information Search on Big Data

Due to the emerging Big Data paradigm, driven by the increasing availability of intelligent services easily accessible by a large number of users (e.g., social networks), traditional data management techniques are inadequate in many real-life scenarios. In particular, the availability of huge amounts of data pertaining to user social interactions, user preferences, and opinions calls for advanced analysis strategies to understand potentially interesting social dynamics. Furthermore, heterogeneity and high speed of user-generated data require suitable data storage and management tools to be designed from scratch. This article presents a framework tailored for analyzing user interactions with intelligent systems while seeking some domain-specific information (e.g., choosing a good restaurant in a visited area). The framework enhances a users quest for information by exploiting previous knowledge about their social environment, the extent of influence the users are potentially subject to, and the influence they may exert on other users. User influence spread across the network is dynamically computed as well to improve user search strategy by providing specific suggestions, represented as tailored faceted features. Such features are the result of data exchange activity (called data posting) that enriches information sources with additional background information and knowledge derived from experiences and behavioral properties of domain experts and users. The approach is tested in an important application scenario such as tourist recommendation, but it can be profitably exploited in several other contexts, for example, viral marketing and food education.

The DEvOTION Algorithm for Delurking in Social Networks

Lurkers are silent members of a social network (SN) who gain benefit from others’ information without significantly giving back to the community. The study of lurking behaviors in SNs is nonetheless important, since these users acquire knowledge from the community, and as such they can be social capital holders. Within this view, a major goal is to delurk such users, i.e., to encourage them to more actively be involved in the SN. Despite the main strategies have been conceptualized in social science and human–computer interaction, no computational approach has been so far defined to turn lurkers into active participants in the SN. In this work we fill this gap by presenting a delurking-oriented targeted influence maximization problem under the linear threshold (LT) model. We define a novel objective function, in terms of the lurking scores associated with the nodes in the final active set, and we show it is monotone and submodular. We provide an approximate solution by developing a greedy algorithm, named DEvOTION, which computes a k-node set that maximizes the value of the delurking-capital-based objective function, for a given lurking threshold. Results on SN datasets of different sizes have demonstrated the significance of our delurking approach via LT-based targeted influence maximization. A comparative evaluation with state-of-the-art algorithms for non-targeted and targeted LT-based influence maximization has also shown the superiority of DEvOTION in terms of delurking capital that is obtained.

A Framework Enhancing the User Search Activity Through Data Posting

Due to the increasing availability of huge amounts of data, traditional data management techniques result inadequate in many real life scenarios. Furthermore, heterogeneity and high speed of this data require suitable data storage and management tools to be designed from scratch. In this paper, we describe a framework tailored for analyzing user interactions with intelligent systems while seeking for some domain specific information (e.g., choosing a good restaurant in a visited area). The framework enhances user quest for information by performing a data exchange activity (called data posting) which enriches the information sources with additional background information and knowledge derived from experiences and behavioral properties of domain experts and users.

How, Who and When: Enhancing Business Process Warehouses By Graph Based Queries

Log analysis and querying recently received a renewed interest from the research community, as the effective understanding of process behavior is crucial for improving business process management. Indeed, currently available log querying tools are not completely satisfactory, especially from the viewpoint of easiness of use. As a matter of fact, there is no framework which meets the requirements of easiness of use, flexibility and efficiency of query evaluation. In this paper, we propose a framework for graphical querying of (process) log data that makes the log analysis task quite easy and efficient, adopting a very general model of process log data which guarantees a high level of flexibility. We implemented our framework by using a flexible storage architecture and a user-friendly data analysis interface, based on an intuitive and yet expressive graph-based query language. Experiments performed on real data confirm the validity of the approach.

Effective High Performance Computing using Peer To Peer Networks

The advances in computational techniques both from a software and hardware viewpoint lead to the development of projects whose complexity could be quite challenging, e.g., biomedical simulations. In order to deal with the increased demand of computational power many collaborative approaches have been proposed in order apply proper partitioning strategy able to assign pieces of execution to a crowd of workers. In this paper, we address this problem in a peer to peer way. We leverage the idling computational resources of users connected to a network. More in detail, we designed a framework that allows users to share their CPU and memory in a secure and efficient way. The latter allows users help each other by asking the network computational resources when they face high computing demanding tasks. We leveraged our solution in a quite intriguing scenario as 3D rendering to validate the scalability and effectiveness of our solution and its profitability for end- users. As we do not require to power additional resources for solving tasks (we better exploit unused resources already powered instead), we hypothesize a remarkable side effect at steady state: energy consumption reduction compared with traditional server farm or cloud based executions.