Considering User Intention in Differential Graph Queries

Empty answers are a major problem by processing pattern matching queries in graph databases. Especially, there can be multiple reasons why a query failed. To support users in such situations, differential queries can be used that deliver missing parts of a graph query. Multiple heuristics are proposed for differential queries, which reduce the search space. Although they are successful in increasing the performance, they can discard query subgraphs relevant to a user. To address this issue, the authors extend the concept of differential queries and introduce top-k differential queries that calculate the ranking based on users’ preferences and significantly support the users’ understanding of query database management systems. A user assigns relevance weights to elements of a graph query that steer the search and are used for the ranking. In this paper the authors propose different strategies for selection of relevance weights and their propagation. As a result, the search is modelled along the most relevant paths. The authors evaluate their solution and both strategies on the DBpedia data graph

Top-k Differential Queries in Graph Databases

The sheer volume as well as the schema complexity of today’s graph databases impede the users in formulating queries against these databases and often cause queries to “fail” by delivering empty answers. To support users in such situations, the concept of differential queries can be used to bridge the gap between an unexpected result (e.g. an empty result set) and the query intention of users. These queries deliver missing parts of a query graph and, therefore, work with such scenarios that require users to specify a query graph. Based on the discovered information about a missing query subgraph, users may understand which vertices and edges are the reasons for queries that unexpectedly return empty answers, and thus can reformulate the queries if needed. A study showed that the result sets of differential queries are often too large to be manually introspected by users and thus a reduction of the number of results and their ranking is required. To address these issues, we extend the concept of differential queries and introduce top-k differential queries that calculate the ranking based on users’ preferences and therefore significantly support the users’ understanding of query database management systems. The idea consists of assigning relevance weights to vertices or edges of a query graph by users that steer the graph search and are used in the scoring function for top-k differential results. Along with the novel concept of the top-k differential queries, we further propose a strategy for propagating relevance weights and we model the search along the most relevant paths

Sheffield University CLEF 2000 submission - bilingual track: German to English

We investigated dictionary based cross language information retrieval using lexical triangulation. Lexical triangulation combines the results of different transitive translations. Transitive translation uses a pivot language to translate between two languages when no direct translation resource is available. We took German queries and translated then via Spanish, or Dutch into English. We compared the results of retrieval experiments using these queries, with other versions created by combining the transitive translations or created by direct translation. Direct dictionary translation of a query introduces considerable ambiguity that damages retrieval, an average precision 79% below monolingual in this research. Transitive translation introduces more ambiguity, giving results worse than 88% below direct translation. We have shown that lexical triangulation between two transitive translations can eliminate much of the additional ambiguity introduced by transitive translation

User-centric privacy preservation in Internet of Things Networks

Recent trends show how the Internet of Things (IoT) and its services are becoming more omnipresent and popular. The end-to-end IoT services that are extensively used include everything from neighborhood discovery to smart home security systems, wearable health monitors, and connected appliances and vehicles. IoT leverages different kinds of networks like Location-based social networks, Mobile edge systems, Digital Twin Networks, and many more to realize these services. Many of these services rely on a constant feed of user information. Depending on the network being used, how this data is processed can vary significantly. The key thing to note is that so much data is collected, and users have little to no control over how extensively their data is used and what information is being used. This causes many privacy concerns, especially for a na ̈ıve user who does not know the implications and consequences of severe privacy breaches. When designing privacy policies, we need to understand the different user data types used in these networks. This includes user profile information, information from their queries used to get services (communication privacy), and location information which is much needed in many on-the-go services. Based on the context of the application, and the service being provided, the user data at risk and the risks themselves vary. First, we dive deep into the networks and understand the different aspects of privacy for user data and the issues faced in each such aspect. We then propose different privacy policies for these networks and focus on two main aspects of designing privacy mechanisms: The quality of service the user expects and the private information from the user’s perspective. The novel contribution here is to focus on what the user thinks and needs instead of fixating on designing privacy policies that only satisfy the third-party applications’ requirement of quality of service

On the Collaboration of an Automatic Path-Planner and a Human User for Path-Finding in Virtual Industrial Scenes

This paper describes a global interactive framework enabling an automatic path-planner and a user to collaborate for finding a path in cluttered virtual environments. First, a collaborative architecture including the user and the planner is described. Then, for real time purpose, a motion planner divided into different steps is presented. First, a preliminary workspace discretization is done without time limitations at the beginning of the simulation. Then, using these pre-computed data, a second algorithm finds a collision free path in real time. Once the path is found, an haptic artificial guidance on the path is provided to the user. The user can then influence the planner by not following the path and automatically order a new path research. The performances are measured on tests based on assembly simulation in CAD scenes

Privacy Games: Optimal User-Centric Data Obfuscation

In this paper, we design user-centric obfuscation mechanisms that impose the minimum utility loss for guaranteeing user's privacy. We optimize utility subject to a joint guarantee of differential privacy (indistinguishability) and distortion privacy (inference error). This double shield of protection limits the information leakage through obfuscation mechanism as well as the posterior inference. We show that the privacy achieved through joint differential-distortion mechanisms against optimal attacks is as large as the maximum privacy that can be achieved by either of these mechanisms separately. Their utility cost is also not larger than what either of the differential or distortion mechanisms imposes. We model the optimization problem as a leader-follower game between the designer of obfuscation mechanism and the potential adversary, and design adaptive mechanisms that anticipate and protect against optimal inference algorithms. Thus, the obfuscation mechanism is optimal against any inference algorithm

Analyzing online search patterns of music festival tourists

Music festivals, as cultural events that induce tourism flows, intermediate both the cultural and travel experience. The present study analyzes online search behavior of potential attenders to a music festival. We hypothesize that the search process reveals latent patterns of behavior of cultural tourists planning to attend music festivals. To this end, information from Google Trends on queries related to three popular music festivals is used to build a network of search topics. Based on it, alternative exponential random graph model specifications are estimated. Findings support the general result of mediated information flows: music festivals induce planning and traveling queries. However, differences relating to the specificities of the cultural event are also found, in particular those regarding what nodes or queries supply the network with more useful information