RRR: Rank-Regret Representative

Selecting the best items in a dataset is a common task in data exploration. However, the concept of "best" lies in the eyes of the beholder: different users may consider different attributes more important, and hence arrive at different rankings. Nevertheless, one can remove "dominated" items and create a "representative" subset of the data set, comprising the "best items" in it. A Pareto-optimal representative is guaranteed to contain the best item of each possible ranking, but it can be almost as big as the full data. Representative can be found if we relax the requirement to include the best item for every possible user, and instead just limit the users' "regret". Existing work defines regret as the loss in score by limiting consideration to the representative instead of the full data set, for any chosen ranking function. However, the score is often not a meaningful number and users may not understand its absolute value. Sometimes small ranges in score can include large fractions of the data set. In contrast, users do understand the notion of rank ordering. Therefore, alternatively, we consider the position of the items in the ranked list for defining the regret and propose the {\em rank-regret representative} as the minimal subset of the data containing at least one of the top-$k$ of any possible ranking function. This problem is NP-complete. We use the geometric interpretation of items to bound their ranks on ranges of functions and to utilize combinatorial geometry notions for developing effective and efficient approximation algorithms for the problem. Experiments on real datasets demonstrate that we can efficiently find small subsets with small rank-regrets

Answering Complex Queries in an Online Community Network

An online community network such as Twitter or amazon.com links entities (e.g., users, products) with various relationships (e.g., friendship, co-purchase) and make such information available for access through a web interface. The web interfaces of these networks often support features such as keyword search and "get-neighbors" — so a visitor can quickly find entities (e.g., users/products) of interest. Nonetheless, the interface is usually too restrictive to answer complex queries such as (1) find 100 Twitter users from California with at least 100 followers who talked about ICWSM last year or (2) find 100 books with at least 200 5-star reviews at amazon.com. In this paper, we introduce the novel problem of answering complex queries that involve non-searchable attributes through the web interface of an online community network. We model such a network as a heterogeneous graph with two access channels, Content Search and Local Search. We propose a unified approach that transforms the complex query into a small number of supported ones based on a strategic query-selection process. We conduct comprehensive experiments on Twitter and amazon.com which demonstrate the efficacy of our proposed algorithms

MobiFace: A Mobile Application for Faceted Search over Hidden Web Databases

A large number of web databases are hidden behind form-based interfaces (consisting of interface components such as textboxes, drop-down boxes, etc) for users to enter their desired values in a search query. Form-based interfaces are not always easy to use and error-prone on mobile devices, mainly because of limitations such as smaller screen, trickier text input, etc. We build MobiFace which is a faceted search system over a hidden database for mobile users. The goal of our system is to help a user to find her tuple of interest with as fewer questions as possible. MobiFace dynamically suggests facets for drilling down into the hidden web database such that the navigation cost is minimized. We run the experiment over real IMDB dataset on a mobile device. 1 2017 IEEE.This contribution was made possible by NPRP grant No. 07-794-1-145 from the Qatar National Research Fund (a member of Qatar Foundation). Any findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsorsScopu

Efficient Communications in Wireless Sensor Networks based on Biological Robustness

Robustness in wireless sensor networks (WSNs) is a critical factor that largely depends on their network topology and on how devices can react to disruptions, including node and link failures. This article presents a novel solution to obtain robust WSNs by exploiting principles of biological robustness at nanoscale. Specifically, we consider Gene Regulatory Networks (GRNs) as a model for the interaction between genes in living organisms. GRNs have evolved over millions of years to provide robustness against adverse factors in cells and their environment. Based on this observation, we apply a method to build robust WSNs, called bio-inspired WSNs, by establishing a correspondence between the topology of GRNs and that of already-deployed WSNs. Through simulation in realistic conditions, we demonstrate that bio-inspired WSNs are more reliable than existing solutions for the design of robust WSNs. We also show that communications in bio-inspired WSNs have lower latency as well as lower energy consumption than the state of the art

Query hidden attributes in social networks

Micro blogs and collaborative content sites such as Twitter and Amazon are popular among millions of users who generate huge numbers of tweets, posts, and reviews every day. Despite their popularity, these sites only provide rudimentary mechanisms to navigate their sites, programmatically or through a browser, like a keyword search interface or a get-neighbors (e.g., Friends) interface. Many interesting queries cannot be directly answered by any of these interfaces, e.g., Find Twitter users in Los Angeles that have tweeted the word 'diabetes' in the last year. Note that the Twitter programming interface does not allow conditions on the user's home location. In this paper, we introduce the novel problem of querying hidden attributes in micro blogs and collaborative content sites by leveraging the existing search mechanisms offered by those sites. We model these data sources as heterogeneous graphs and their two key access interfaces, Local Search and Content Search, which search through keywords and neighbors respectively. We show which of these two approaches is better for which types of hidden attribute searches. We conduct experiments on Twitter, Amazon, and Rate MDs to evaluate the performance of the search approaches. 2014 IEEE.Scopu