In many applications, entity references (i.e., records) and entities need to be organized to capture diverse relationships like type-subtype, is-A (mapping entities to types), and duplicate (mapping records to entities) relationships. However, automatic identification of such relationships is often inaccurate due to noise and heterogeneous representation of records across sources. Similarly, manual maintenance of these relationships is infeasible and does not scale to large datasets. In this work, we circumvent these challenges by considering weak supervision in the form of an oracle to formulate a novel hierarchical ER task. In this setting, records are clustered in a tree-like structure containing records at leaf-level and capturing record-entity (duplicate), entity-type (is-A) and subtype-supertype relationships. For effective use of supervision, we leverage triplet comparison oracle queries that take three records as input and output the most similar pair(s). We develop HierER, a querying strategy that uses record pair similarities to minimize the number of oracle queries while maximizing the identified hierarchical structure. We show theoretically and empirically that HierER is effective under different similarity noise models and demonstrate empirically that HierER can scale up to million-size datasets.

One of the greatest successes of computational complexity theory is the classification of countless fundamental computational problems into polynomial-time and NP-hard ones, two classes that are often referred to as tractable and intractable, respectively. However, this crude distinction of algorithmic efficiency is clearly insufficient when handling today's large scale of data. We need a finer-grained design and analysis of algorithms that pinpoints the exact exponent of polynomial running time, and a better understanding of when a speed-up is not possible. Based on stronger complexity assumptions than P vs NP, like the Strong Exponential Time Hypothesis, recently conditional lower bounds for a variety of fundamental problems in P have been proposed. Unfortunately, these conditional lower bounds often break down when one may settle for a near-optimal solution. Indeed, approximation algorithms can play a significant role when designing fast algorithms not just for traditional NP Hard problems, but also for polynomial time problems. For some applications arising in machine learning, the time complexity of the underlying algorithms is not sufficient to ensure a fast solution. It is often needed to collect side information about the data to ensure high accuracy. This requires low query complexity. In this presentation, we will cover new facets of fast algorithm design for large scale data analysis that emphasizes on the role of developing approximation algorithms for better polynomial time/query complexity.

Blocking is a key component of Entity Resolution (ER) that aims to improve efficiency by quickly pruning out non-matching record pairs. However, depending on the noise in the dataset and the distribution of entity cluster sizes, existing techniques can be either (a) too aggressive, such that they help scale but can adversely affect the ER effectiveness, or (b) too permissive, potentially harming ER efficiency. We propose a new methodology of progressive blocking that enables both efficient and effective ER and works across different entity cluster size distributions without manual fine tuning. In this paper, we demonstrate BEER (Blocking for Effective Entity Resolution), the first end-to-end system that leverages intermediate ER output in a feedback loop to refine the blocking result in a data-driven fashion, thereby enabling effective entity resolution. BEER allows the user to explore the different components of the ER pipeline, analyze the effectiveness of alternative blocking techniques and understand the interaction between blocking and ER. BEER supports visualization of the different entities present in a block, explains the change in blocking output with every round of feedback and allows the end-user to interactively compare different techniques. BEER has been developed as open-source software; the code and the demonstration video are available at beer-system.github.io.

Metric based comparison operations such as finding maximum, nearest and farthest neighbor are fundamental to studying various clustering techniques such as k -center clustering and agglomerative hierarchical clustering. These techniques crucially rely on accurate estimation of pairwise distance between records. However, computing exact features of the records, and their pairwise distances is often challenging, and sometimes not possible. We circumvent this challenge by leveraging weak supervision in the form of a comparison oracle that compares the relative distance between the queried points such as `Is point u closer to v or w closer to x ?'. However, it is possible that some queries are easier to answer than others using a comparison oracle. We capture this by introducing two different noise models called adversarial and probabilistic noise. In this paper, we study various problems that include finding maximum, nearest/farthest neighbor search under these noise models. Building upon the techniques we develop for these problems, we give robust algorithms for k -center clustering and agglomerative hierarchical clustering. We prove that our algorithms achieve good approximation guarantees with a high probability and analyze their query complexity. We evaluate the effectiveness and efficiency of our techniques empirically on various real-world datasets.

Automatically matching reviewers to papers is a crucial step of the peer review process for venues receiving thousands of submissions. Unfortunately, common paper matching algorithms often construct matchings suffering from two critical problems: (1) the group of reviewers assigned to a paper do not collectively possess sufficient expertise, and (2) reviewer workloads are highly skewed. In this paper, we propose a novel local fairness formulation of paper matching that directly addresses both of these issues. Since optimizing our formulation is not always tractable, we introduce two new algorithms, FairIR and FairFlow, for computing fair matchings that approximately optimize the new formulation. FairIR solves a relaxation of the local fairness formulation and then employs a rounding technique to construct a valid matching that provably maximizes the objective and only compromises on fairness with respect to reviewer loads and papers by a small constant. In contrast, FairFlow is not provably guaranteed to produce fair matchings, however it can be 2x as efficient as FairIR and an order of magnitude faster than matching algorithms that directly optimize for fairness. Empirically, we demonstrate that both FairIR and FairFlow improve fairness over standard matching algorithms on real conference data. Moreover, in comparison to state-of-the-art matching algorithms that optimize for fairness only, FairIR achieves higher objective scores, FairFlow achieves competitive fairness, and both are capable of more evenly allocating reviewers.

Entity resolution (ER) seeks to identify which records in a data set refer to the same real-world entity. Given the diversity of ways in which entities can be represented, matched and distinguished, ER is known to be a challenging task for automated strategies, but relatively easier for expert humans. In our work, we abstract the knowledge of experts with the notion of a binary oracle. Our oracle can answer questions of the form "do records u and v refer to the same entity?" under a flexible error model, allowing for some questions to be more difficult to answer correctly than others.

Entity resolution (ER) is the task of identifying all records in a database that refer to the same underlying entity. This is an expensive task, and can take a significant amount of money and time; the end-user may want to take decisions during the process, rather than waiting for the task to be completed. We formalize an online version of the entity resolution task, and use an oracle which correctly labels matching and non-matching pairs through queries. In this setting, we design algorithms that seek to maximize progressive recall, and develop a novel analysis framework for prior proposals on entity resolution with an oracle, beyond their worst case guarantees. Finally, we provide both theoretical and experimental analysis of the proposed algorithms.

In this paper, we introduce a natural generalization of Weighted Set Cover and Maximum Coverage, called Size-Constrained Weighted Set Cover. The input is a collection of n elements, a collection of weighted sets over the elements, a size constraint k, and a minimum coverage fraction ŝ; the output is a sub-collection of up to k sets whose union contains at least ŝn elements and whose sum of weights is minimal. We prove the hardness of approximation of this problem, and we present efficient approximation algorithms with provable quality guarantees that are the best possible. In many applications, the elements are data records, and the set collection to choose from is derived from combinations (patterns) of attribute values. We provide optimization techniques for this special case. Finally, we experimentally demonstrate the effectiveness and efficiency of our solutions.

Semi-structured data are prevalent on the web, with formats such as XML and JSON soaring in popularity due to their generality, flexibility and easy customization. However, these very same features make semi-structured data prone to a range of data quality errors, from errors in content to errors in structure. While the former has been well studied, little attention has been paid to structural errors. In this demonstration, we present TreeScope, which analyzes semi-structured data sets with the goal of automatically identifyingstructural anomaliesfrom the data. Our techniques learn robust structural models that have high support, to identify potential errors in the structure. Identified structural anomalies are then concisely summarized to provide plausible explanations of the potential errors. The goal of this demonstration is to enable an interactive exploration of the process of identifying and summarizing structural anomalies in semi-structured data sets.

In our Big Data era, data is being generated, collected and analyzed at an unprecedented scale, and data-driven decision making is sweeping through all aspects of society. Recent studies have shown that poor quality data is prevalent in large databases and on the Web. Since poor quality data can have serious consequences on the results of data analyses, the importance of veracity, the fourth `V' of big data is increasingly being recognized. In this tutorial, we highlight the substantial challenges that the first three `V's, volume, velocity and variety, bring to dealing with veracity in big data. Due to the sheer volume and velocity of data, one needs to understand and (possibly) repair erroneous data in a scalable and timely manner. With the variety of data, often from a diversity of sources, data quality rules cannot be specified a priori; one needs to let the “data to speak for itself” in order to discover the semantics of the data. This tutorial presents recent results that are relevant to big data quality management, focusing on the two major dimensions of (i) discovering quality issues from the data itself, and (ii) trading-off accuracy vs efficiency, and identifies a range of open problems for the community.

With the widespread use of shared-nothing clusters of servers, there has been a proliferation of distributed object stores that offer high availability, reliability and enhanced performance for MapReduce-style workloads. However, data-intensive scientific workflows and join-intensive queries cannot always be evaluated efficiently using MapReduce-style processing without extensive data migrations, which cause network congestion and reduced query throughput. In this paper, we study the problem of computing data placement strategies that minimize the data communication costs incurred by such workloads in a distributed setting.

Many applications process data in which there exists a “conservation law” between related quantities. For example, in traffic monitoring, every incoming event, such as a packet's entering a router or a car's entering an intersection, should ideally have an immediate outgoing counterpart. We propose a new class of constraints-Conservation Rules-that express the semantics and characterize the data quality of such applications. We give confidence metrics that quantify how strongly a conservation rule holds and present approximation algorithms (with error guarantees) for the problem of discovering a concise summary of subsets of the data that satisfy a given conservation rule. Using real data, we demonstrate the utility of conservation rules and we show order-of-magnitude performance improvements of our discovery algorithms over naive approaches.

Semi-structured data such as XML are popular for data interchange and storage. However, many XML documents have improper nesting where open - and close-tags are unmatched. Since some semi-structured data (e.g., Latex) have a flexible grammar and since many XML documents lack an accompanying DTD or XSD, we focus on computing a syntactic repair via the edit distance. To solve this problem, we propose a dynamic programming algorithm which takes cubic time. While this algorithm is not scalable, well-formed substrings of the data can be pruned to enable faster computation. Unfortunately, there are still cases where the dynamic program could be very expensive; hence, we give branch-and-bound algorithms based on various combinations of two heuristics, called MinCost and MaxBenefit, that trade off between accuracy and efficiency. Finally, we experimentally demonstrate the performance of these algorithms on real data.

Many applications process data in which there exists a ``conservation law'' between related quantities. For example, in traffic monitoring, every incoming event, such as a packet's entering a router or a car's entering an intersection, should ideally have an immediate outgoing counterpart. We propose a new class of constraints -- Conservation Rules -- that express the semantics and characterize the data quality of such applications. We give confidence metrics that quantify how strongly a conservation rule holds and present approximation algorithms (with error guarantees) for the problem of discovering a concise summary of subsets of the data that satisfy a given conservation rule. Using real data, we demonstrate the utility of conservation rules and we show order-of-magnitude performance improvements of our discovery algorithms over naive approaches.

We are often thrilled by the abundance of information surrounding us and wish to integrate data from as many sources as possible. However, understanding, analyzing, and using these data are often hard. Too much data can introduce a huge integration cost, such as expenses for purchasing data and resources for integration and cleaning. Furthermore, including low-quality data can even deteriorate the quality of integration results instead of bringing the desired quality gain. Thus, "the more the better" does not always hold for data integration and often "less is more". In this paper, we study how to select a subset of sources before integration such that we can balance the quality of integrated data and integration cost. Inspired by the Marginalism principle in economic theory, we wish to integrate a new source only if its marginal gain, often a function of improved integration quality, is higher than the marginal cost, associated with data-purchase expense and integration resources. As a first step towards this goal, we focus on data fusion tasks, where the goal is to resolve conflicts from different sources. We propose a randomized solution for selecting sources for fusion and show empirically its effectiveness and scalability on both real-world data and synthetic data.

We introduce schema covering, the problem of identifying easily understandable common objects for describing large and complex schemas. Defining transformations between schemas is a key objective in information integration. However, this process often becomes cumbersome when the schemas are large and structurally complex. If such complex schemas can be broken into smaller and simpler objects, then simple transformations defined over these smaller objects can be reused to define suitable transformations among the complex schemas. Schema covering performs this vital task by identifying a collection of common concepts from a repository and creating a cover of the complex schema by these concepts. In this paper, we formulate the problem of schema covering, show that it is NP-Complete, and give efficient approximation algorithms for it. A performance evaluation with real business schemas confirms the effectiveness of our approach.

We generalize the graph streaming model to hypergraphs. In this streaming model, hyperedges are arriving online and any computation has to be done on-the-fly using a small amount of space. Each hyperedge can be viewed as a set of elements (nodes), so we refer to our proposed model as the “set-streaming” model of computation. We consider the problem of “maximum coverage”, in which k sets have to be selected that maximize the total weight of the covered elements. In the set-streaming model of computation, we show that our algorithm for maximum-coverage achieves an approximation factor of ¼. When multiple passes are allowed, we also provide a ⊖(log n) approximation algorithm for the set-cover. We next consider a multi-topic blog-watch application, an extension of blog-alert like applications for handling simultaneous multiple-topic requests. We show how the problems of maximum-coverage and set-cover in the set-streaming model can be utilized to give efficient online solutions to this problem. We verify the effectiveness of our methods both on synthetic and real weblog data.

The dramatic growth in the number of application domains that naturally generate probabilistic, uncertain data has resulted in a need for efficiently supporting complex querying and decision-making over such data. In this paper, we present a unified approach to ranking and top-k query processing in probabilistic databases by viewing it as a multi-criteria optimization problem, and by deriving a set of features that capture the key properties of a probabilistic dataset that dictate the ranked result. We contend that a single, specific ranking function may not suffice for probabilistic databases, and we instead propose two parameterized ranking functions , called PRF ω and PRF e , that generalize or can approximate many of the previously proposed ranking functions. We present novel generating functions -based algorithms for efficiently ranking large datasets according to these ranking functions, even if the datasets exhibit complex correlations modeled using probabilistic and/xor trees or Markov networks . We further propose that the parameters of the ranking function be learned from user preferences, and we develop an approach to learn those parameters. Finally, we present a comprehensive experimental study that illustrates the effectiveness of our parameterized ranking functions, especially PRF e , at approximating other ranking functions and the scalability of our proposed algorithms for exact or approximate ranking.

We present an efficient dynamic algorithm for clustering undirected graphs, whose edge property is changing continuously. The algorithm maintains clusters of high quality in presence of insertion and deletion (update) of edges. The algorithm is motivated by the minimum-cut tree based partitioning algorithm of [3] and [4]. It takes O(k3) time for each update processing, where k is the maximum size of any cluster. This is the worst case time complexity, and in general update time taken is much less. The clusters satisfy the bicriteria for quality guarantee proposed in [3].