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, 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 a large portion of data. A much smaller representative can be found if we relax the requirement of including the best item for each 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 dataset, 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 dataset. In contrast, users do understand the notion of rank ordering. Therefore, we consider items’ positions in the ranked list in defining the regret and propose the 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 polynomial time solvable in two-dimensional space but is NP-hard on three or more dimensions. We design a suite of algorithms to fulfill different purposes, such as whether relaxation is permitted on k , the result size, or both, whether a distribution is known, whether theoretical guarantees or practical efficiency is important, and so on. Experiments on real datasets demonstrate that we can efficiently find small subsets with small rank-regrets.

Learning graph generative models is a challenging task for deep learning and has wide applicability to a range of domains like chemistry, biology and social science. However current deep neural methods suffer from limited scalability: for a graph with n nodes and m edges, existing deep neural methods require Omega(n^2) complexity by building up the adjacency matrix. On the other hand, many real world graphs are actually sparse in the sense that m << n^2. Based on this, we develop a novel autoregressive model, named BiGG, that utilizes this sparsity to avoid generating the full adjacency matrix, and importantly reduces the graph generation time complexity to O((n + m) log n). Furthermore, during training this autoregressive model can be parallelized with O(log n) synchronization stages, which makes it much more efficient than other autoregressive models that require Omega(n). Experiments on several benchmarks show that the proposed approach not only scales to orders of magnitude larger graphs than previously possible with deep autoregressive graph generative models, but also yields better graph generation quality.

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, 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 a large portion of data. A much smaller representative can be found if we relax the requirement to include the best item for each 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, 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 a 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.

Query optimizers depend on selectivity estimates of query predicates to produce a good execution plan. When a query contains multiple predicates, today's optimizers use a variety of assumptions, such as independence between predicates, to estimate selectivity. While such techniques have the benefit of fast estimation and small memory footprint, they often incur large selectivity estimation errors. In this work, we reconsider selectivity estimation as a regression problem. We explore application of neural networks and tree-based ensembles to the important problem of selectivity estimation of multi-dimensional range predicates. While their straightforward application does not outperform even simple baselines, we propose two simple yet effective design choices, i.e., regression label transformation and feature engineering, motivated by the selectivity estimation context. Through extensive empirical evaluation across a variety of datasets, we show that the proposed models deliver both highly accurate estimates as well as fast estimation.

Signal reconstruction problem (SRP) is an important optimization problem where the objective is to identify a solution to an underdetermined system of linear equations that is closest to a given prior. It has a substantial number of applications in diverse areas including network traffic engineering, medical image reconstruction, acoustics, astronomy and many more. Most common approaches for SRP do not scale to large problem sizes. In this paper, we propose a dual formulation of this problem and show how adapting database techniques developed for scalable similarity joins provides a significant speedup. Extensive experiments on real-world and synthetic data show that our approach produces a significant speedup of up to 20x over competing approaches.

Efficiently estimating the inclusion coefficient - the fraction of values of one column that are contained in another column - is useful for tasks such as data profiling and foreign-key detection. We present a new estimator, BML, for inclusion coefficient based on Hyperloglog sketches that results in significantly lower error compared to the state-of-the art approach that uses Bottom-k sketches. We evaluate the error of the BML estimator using experiments on industry benchmarks such as TPC-H and TPC-DS, and several real-world databases. As an independent contribution, we show how Hyperloglog sketches can be maintained incrementally with data deletions using only a constant amount of additional memory.

Finding the maxima of a database based on a user preference, especially when the ranking function is a linear combination of the attributes, has been the subject of recent research. A critical observation is that the em convex hull is the subset of tuples that can be used to find the maxima of any linear function. However, in real world applications the convex hull can be a significant portion of the database, and thus its performance is greatly reduced. Thus, computing a subset limited to $r$ tuples that minimizes the regret ratio (a measure of the user's dissatisfaction with the result from the limited set versus the one from the entire database) is of interest.

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.

Over the past few decades as purchasing options moved online, the widespread use and popularity of online review sites has simultaneously increased. In spite of the fact that a huge extent of buying choices today are driven by numeric scores (e.g., rating a product), detailed reviews play an important role for activities like purchasing an expensive DSLR camera. Since writing a detailed review for an item is usually time-consuming, the number of reviews available in the Web is far from many. In this paper, we build a system AD-WIRE that given a user and an item, our system identifies the top- k meaningful tags to help her review the item easily. AD-WIRE allows a user to compose her review by quickly selecting from among the set of returned tags or writes her own review. AD-WIRE also visualizes the dependency of the tags to different aspects of an item so a user can make an informed decision quickly. The system can be used for different type of the products. The current demonstration is built to explore review writing process for the mobile phones.

Gene Regulatory Networks (GRNs) represent the interactions of genes in living organisms, which have evolved over millions of years to provide a near-optimal structure for rapid adaptation to the environment. On the other hand, robustness in wireless sensor networks (WSNs) is a critical factor that largely depends on their topology and how quickly the network can recover from node and link failures. This article proposes a novel approach to design robust WSNs by exploiting GRNs. Specifically, we build bio-inspired WSNs based on the topology of GRNs. Our approach embeds the physical communication graph of the WSN into the GRN graph under the optimization criterion of minimizing the interference between different nodes. Furthermore, we propose an algorithm to identify data collection points (i.e., sinks) and improve robustness by maximizing the expansion of the network. Through an analytical evaluation, we show that our bio-inspired graph embedding approach leads to robust WSNs which preserve the structural properties of GRNs.

An online community network such as Twitter, Yelp or amazon.com links entities (e.g., Users, products) with various relationships (e.g., Friendship, co-purchase, co-review) and make such information available for access through a web interface. Often, these community networks act as "social sensors" in which users sense information in the real world and mention them online. The web interfaces of these networks often support features such as keyword search that allow an user to quickly find entities of interest. While these interfaces are adequate for regular users, they are often too restrictive to answer complex queries such as (1) find 100 Twitter users from California with at least 100 followers who talked about earthquakes last year or (2) find 25 restaurants in Yelp with at least 10 5-star reviews with 10 or more 'useful' points. In this paper, we investigate the 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 number of efficient algorithms that leverage properties of the heterogeneous graph and also propose a strategy selection algorithm based on the concept of multi-armed bandits. We conduct comprehensive experiments over popular social sensing websites such as Twitter and amazon.com which demonstrate the efficacy of our proposed algorithms.

The increasing popularity and widespread use of online review sites over the past decade has motivated businesses of all types to possess an expansive arsenal of user feedback (preferably positive) in order to mark their reputation and presence in the Web. Though a significant proportion of purchasing decisions today are driven by average numeric scores (e.g., movie rating in IMDB), detailed reviews are critical for activities such as buying an expensive digital SLR camera, reserving a vacation package, etc. Since writing a detailed review for a product (or, a service) is usually time-consuming and may not offer any incentive, the number of useful reviews available in the Web is far from many. The corpus of reviews available at our disposal for making informed decisions also suffers from spam and misleading content, typographical and grammatical errors, etc. In this paper, we address the problem of how to engage the lurkers (i.e., people who read reviews but never take time and effort to write one) to participate and write online reviews by systematically simplifying the reviewing task. Given a user and an item that she wants to review, the task is to identify the top-$k$ meaningful phrases (i.e., tags) from the set of all tags (i.e., available user feedback for items) that, when advised, would help her review an item easily. We refer to it as the TagAdvisor problem, and formulate it as a general-constrained optimization goal. Our framework is centered around three measures - relevance (i.e., how well the result set of tags describes an item to a user), coverage (i.e., how well the result set of tags covers the different aspects of an item), and polarity (i.e., how well sentiment is attached to the result set of tags) in order to help a user review an item satisfactorily. By adopting different definitions of coverage, we identify two concrete problem instances that enable a wide range of real-world scenarios. We show that these problems are NP-hard and develop practical algorithms with theoretical bounds to solve them efficiently. We conduct detailed experiments on synthetic and real data crawled from the web to validate the utility of our problem and effectiveness of our solutions.

In this paper, we introduce a novel, general purpose, technique for faster sampling of nodes over an online social network. Specifically, unlike traditional random walks which wait for the convergence of sampling distribution to a predetermined target distribution - a waiting process that incurs a high query cost - we develop WALK-ESTIMATE, which starts with a much shorter random walk, and then proactively estimate the sampling probability for the node taken before using acceptance-rejection sampling to adjust the sampling probability to the predetermined target distribution. We present a novel backward random walk technique which provides provably unbiased estimations for the sampling probability, and demonstrate the superiority of WALK-ESTIMATE over traditional random walks through theoretical analysis and extensive experiments over real world online social networks.