Prescriptions, or actionable recommendations, are commonly generated across various fields to influence key outcomes such as improving public health, enhancing economic policies, or increasing business efficiency. While traditional association-based methods may identify correlations, they often fail to reveal the underlying causal factors needed for informed decision-making. On the other hand, in decision making for tasks with significant societal or economic impact, it is crucial to provide recommendations that are interpretable and justifiable, and equitable in terms of the outcome for both the protected and non-protected groups. Motivated by these two goals, this paper introduces a fairness-aware framework leveraging causal reasoning for generating a set of interpretable and actionable prescription rules (ruleset) toward betterment of an outcome while preventing exacerbating inequalities for protected groups. By considering group and individual fairness metrics from the literature, we ensure that both protected and non-protected groups benefit from these recommendations, providing a balanced and equitable approach to decision-making. We employ efficient optimizations to explore the vast and complex search space considering both fairness and coverage of the prescription ruleset. Empirical evaluation and case study on real-world datasets demonstrates the utility of our framework for different use cases.

The demonstration presents ClaimIt — a tool for extracting views that support a user-provided claim. Such views can assist users in finding evidence of phenomena of interest, criticizing given claims by proposing opposing viewpoints, inspecting the robustness of statements with respect to subpopulations, and so on. To be useful, the view should constitute a "natural" characterization of a significant subpopulation. In a recently published work, we focused on claims that compare groups by an aggregate query, and explored the measurement of naturalness as well as the algorithmic challenge of handling the plenitude of possible views. ClaimIt realizes the framework as an interactive system that enables users to phrase their claims in a convenient user interface, extract supporting views, sort them by different measures of naturalness, and control the weights of individual measures in a global ranking function. In the demonstration of ClaimIt, the audience will be able to suggest and analyze different claims on various datasets. A companion video for this submission is available at https://youtu.be/k2KxyeHzRq8.

Causal inference aids researchers in discovering cause-and-effect relationships, leading to scientific insights. Accurate causal estimation requires identifying confounding variables to avoid false discoveries. Pearl's causal model uses causal DAGs to identify confounding variables, but incorrect DAGs can lead to unreliable causal conclusions. However, for high dimensional data, the causal DAGs are often complex beyond human verifiability. Graph summarization is a logical next step, but current methods for general-purpose graph summarization are inadequate for causal DAG summarization. This paper addresses these challenges by proposing a causal graph summarization objective that balances graph simplification for better understanding while retaining essential causal information for reliable inference. We develop an efficient greedy algorithm and show that summary causal DAGs can be directly used for inference and are more robust to misspecification of assumptions, enhancing robustness for causal inference. Experimenting with six real-life datasets, we compared our algorithm to three existing solutions, showing its effectiveness in handling high-dimensional data and its ability to generate summary DAGs that ensure both reliable causal inference and robustness against misspecifications.

SQL queries with group-by and average are frequently used and plotted as bar charts in several data analysis applications. Understanding the reasons behind the results in such an aggregate view may be a highly nontrivial and time-consuming task, especially for large datasets with multiple attributes. Hence, generating automated explanations for aggregate views can allow users to gain better insights into the results while saving time in data analysis. When providing explanations for such views, it is paramount to ensure that they are succinct yet comprehensive, reveal different types of insights that hold for different aggregate answers in the view, and, most importantly, they reflect reality and arm users to make informed data-driven decisions, i.e., the explanations do not only consider correlations but are causal. In this paper, we present CauSumX, a framework for generating summarized causal explanations for the entire aggregate view. Using background knowledge captured in a causal DAG, CauSumX finds the most effective causal treatments for different groups in the view. We formally define the framework and the optimization problem, study its complexity, and devise an efficient algorithm using the Apriori algorithm, LP rounding, and several optimizations. We experimentally show that our system generates useful summarized causal explanations compared to prior work and scales well for large high-dimensional data.

Recent studies investigated the challenge of assessing the strength of a given claim extracted from a dataset, particularly the claim's potential of being misleading and cherry-picked. We focus on claims that compare answers to an aggregate query posed on a view that selects tuples. The strength of a claim amounts to the question of how likely it is that the view is carefully chosen to support the claim, whereas less careful choices would lead to contradictory claims. We embark on the study of the reverse task that offers a complementary angle in the critical assessment of data-based claims: given a claim, find useful supporting views. The goal of this task is twofold. On the one hand, we aim to assist users in finding significant evidence of phenomena of interest. On the other hand, we wish to provide them with machinery to criticize or counter given claims by extracting evidence of opposing statements. To be effective, the supporting sub-population should be significant and defined by a "natural" view. We discuss several measures of naturalness and propose ways of extracting the best views under each measure (and combinations thereof). The main challenge is the computational cost, as naïve search is infeasible. We devise anytime algorithms that deploy two main steps: (1) a preliminary construction of a ranked list of attribute combinations that are assessed using fast-to-compute features, and (2) an efficient search for the actual views based on each attribute combination. We present a thorough experimental study that shows the effectiveness of our algorithms in terms of quality and execution cost. We also present a user study to assess the usefulness of the naturalness measures.

Data preparation is an essential step in every data-related effort, from scientific projects in academia to data-driven decision-making in industry. Typically, data preparation is not an interesting piece of a project --- it transforms raw data into a format that enables further innovative work. Because such scripts are never intended to be interesting, are project-specific, and are written in general-purpose languages, they can be tedious to understand and difficult to verify. As a result, data preparation scripts can easily become a breeding ground for poor engineering and statistical practices. Ideally, data preparation scripts are "admirably boring" --- they should serve the project, but otherwise be as simple and as standard as possible. We propose a bottom-up script standardization framework that takes a user's data preparation script and transforms it into a simpler, more standardized version of itself. Our framework takes the user's script not as an unchangeable definition of correctness, but as a sketch of the user's intent. We embedded this framework in a system called LucidScript.

Causal inference can quantify cause-effect relationships in domains as varied as medicine, economics and public policy. Production computer systems exhibit a similar level of complexity and a recurring need to diagnose problems quickly. However, systems are only observed imperfectly, often via long, messy, semi-structured logs. In this work, we want to accelerate large systems debugging by applying causal inference over logs, enabling engineers to diagnose problems and assess interventions in a principled manner. Our framework achieves this through two human-in-the-loop modules: (1) The Candidate Cause Ranker , through which one can determine the causes of a variable without running a full causal discovery algorithm; and (2) the Interactive Causal Graph Refiner , which helps engineers compute an unbiased estimation of their effect of interest without extensive manual causal graph verification. Both modules are powered by the insight that only part of the causal graph of the system is needed to correctly quantify a given effect of interest. We also provide a data preparation pipeline, the Log Converter , which transforms raw, messy, real-world logs into an appropriate tabular input for causal inference, using methods drawn from data transformation, cleaning, and extraction. We evaluate LOGos, a prototype implementation, on both real-world and synthetic logs and find that: (1) The Candidate Cause Ranker achieved an average precision 1.08×-18× higher than the baselines, in interactive time; (2) The Interactive Causal Graph Refiner required a number of causal judgments 1.61 × - 16.83× lower than the baselines; and (3) The latency of Log Converter scaled linearly with three measures of the complexity of a log: length, distinct templates, and fraction of tokens that are variables.

When analyzing large datasets, analysts are often interested in the explanations for unexpected results produced by their queries. In this work, we focus on aggregate SQL queries that expose correlations in the data. A major challenge that hinders the interpretation of such queries is confounding bias, which can lead to an unexpected correlation. We generate explanations in terms of a set of potential confounding variables that explain the unexpected correlation observed in a query. We propose to mine candidate confounding variables from external sources since, in many real-life scenarios, the explanations are not solely contained in the input data. We present an efficient algorithm that finds a concise subset of attributes (mined from external sources and the input dataset) that explain the unexpected correlation. This algorithm is embodied in a system called MESA. We demonstrate experimentally over multiple real-life datasets and through a user study that our approach generates insightful explanations, outperforming existing methods even when are given with the extracted attributes. We further demonstrate the robustness of our system to missing data and the ability of MESA to handle input datasets containing millions of tuples and an extensive search space of candidate confounding attributes.

Causal inference is fundamental to empirical scientific discoveries in natural and social sciences; however, in the process of conducting causal inference, data management problems can lead to false discoveries. Two such problems are (i) not having all attributes required for analysis, and (ii) misidentifying which attributes are to be included in the analysis. Analysts often only have access to partial data, and they critically rely on (often unavailable or incomplete) domain knowledge to identify attributes to include for analysis, which is often given in the form of a causal DAG. We argue that data management techniques can surmount both of these challenges. In this work, we introduce the Causal Data Integration (CDI) problem, in which unobserved attributes are mined from external sources and a corresponding causal DAG is automatically built. We identify key challenges and research opportunities in designing a CDI system, and present a system architecture for solving the CDI problem. Our preliminary experimental results demonstrate that solving CDI is achievable and pave the way for future research.

Data analytics often make sense of large data sets by generalization: aggregating from the detailed data to a more general context. Given a dataset, misleading generalizations can sometimes be drawn from a cherry-picked level of aggregation to obscure substantial subgroups that oppose the generalization. Our goal is to detect and explain cherry-picked generalizations by refining the corresponding aggregate queries. We demonstrate OREO, a system to compute a support score of the given statement to quantify the quality of the generalization; that is, whether the aggregated result is an accurate reflection of the data. To better understand the resulting score, our system also identifies significant counterexamples and alternative statements that better represent the data at hand. We will demonstrate the utility of OREO for investigating generalizations, by interacting with the VLDB'22 participants who will use the OREO interface for statement validation and explanation.

We demonstrate EDA4Sum, a framework dedicated to generating guided multi-step data summarization pipelines for very large datasets. Data summarization is the process of producing interpretable and representative subsets of an input dataset. It is usually performed following a one-shot process with the purpose of finding the best summary. EDA4Sum leverages Exploratory Data Analysis (EDA) to produce connected summaries in multiple steps, with the goal of maximizing their cumulative utility. A useful summary contains k individually uniform sets that are collectively diverse to be representative of the input data. EDA4Sum accommodates datasets with different characteristics by providing the ability to tune the weights of uniformity, diversity and novelty when generating multi-step summaries. We demonstrate the superiority of multi-step EDA summarization over single-step summarization for summarizing very large data, and the need to provide guidance to domain experts, by interacting with the VLDB'22 participants who will act as data analysts. The application is avilable at https://bit.ly/eda4sum_application.

Data summarization is the process of producing interpretable and representative subsets of an input dataset. It is usually performed following a one-shot process with the purpose of finding the best summary. A useful summary contains k individually uniform sets that are collectively diverse to be representative. Uniformity addresses interpretability and diversity addresses representativity. Finding such as summary is a difficult task when data is highly diverse and large. We examine the applicability of Exploratory Data Analysis (EDA) to data summarization and formalize Eda4Sum, the problem of guided exploration of data summaries that seeks to sequentially produce connected summaries with the goal of maximizing their cumulative utility. Eda4Sum generalizes one-shot summarization. We propose to solve it with one of two approaches: (i) Top1Sum that chooses the most useful summary at each step; (ii) RLSum that trains a policy with Deep Reinforcement Learning that rewards an agent for finding a diverse and new collection of uniform sets at each step. We compare these approaches with one-shot summarization and top-performing EDA solutions. We run extensive experiments on three large datasets. Our results demonstrate the superiority of our approaches for summarizing very large data, and the need to provide guidance to domain experts.

We demonstrate SubDEx, a dedicated framework for Subjective Data Exploration (SDE). SubDEx enables the joint exploration of items, people, and people's opinions on items, in a guided multi-step process where each step aggregates the most useful and diverse trends in the form of rating maps. Because of the large search space of possible rating maps, we leverage pruning strategies to enable interactive running times. We demonstrate the need for a dedicated SDE framework and the effectiveness and efficiency of our approach, by interacting with the ICDE'21 participants who will act as data analysts.

The problem of finding an item-set of maximal aggregated utility that satisfies a set of constraints is at the cornerstone of many search applications. Its classical definition assumes that all the information needed to verify the constraints is explicitly given. However, in real-world databases, the data available on items is often partial. Hence, adequately answering constrained search queries requires the completion of this missing information. A common approach to complete missing data is to employ Machine Learning (ML)-based inference. However, such methods are naturally error-prone. More accurate data can be obtained by asking humans to complete missing information. But, as the number of items in the repository is vast, limiting human effort is crucial. To this end, we introduce the Probabilistic Constrained Search (PCS) problem, which identifies a bounded-size item-set whose data completion is likely to be highly beneficial, as these items are expected to belong to the result set of the constrained search queries in question. We prove PCS to be hard to approximate, and consequently propose a best-effort PTIME heuristic to solve it. We demonstrate the effectiveness and efficiency of our algorithm over real-world datasets and scenarios, showing that our algorithm significantly improves the result sets of constrained search queries, in terms of both utility and constraints satisfaction probability.

Subjective data links people to content items and reflects who likes or dislikes what. The valuable information this data contains is virtually infinite and satisfies various information needs. Yet, as of today, dedicated tools to explore this data are lacking. In this paper, we develop a framework for Subjective Data Exploration (SDE). Our solution enables the joint exploration of items, people, and people's opinions on items, in a guided multi-step process where each step aggregates the most useful and diverse trends in the form of rating maps. Because of the large search space of possible rating maps, we leverage pruning strategies based on confidence intervals and multi-armed bandits. Our large-scale experiments with human subjects and real datasets, demonstrate the need for dedicated SDE frameworks and the effectiveness and efficiency of our approach.

Generalizing from detailed data to statements in a broader context is often critical for users to make sense of large data sets. Correspondingly, poorly constructed generalizations might convey misleading information even if the statements are technically supported by the data. For example, a cherry-picked level of aggregation could obscure substantial sub-groups that oppose the generalization. We present a framework for detecting and explaining cherry-picked generalizations by refining aggregate queries. We present a scoring method to indicate the appropriateness of the generalizations. We design efficient algorithms for score computation. For providing a better understanding of the resulting score, we also formulate practical explanation tasks to disclose significant counterexamples and provide better alternatives to the statement. We conduct experiments using real-world data sets and examples to show the effectiveness of our proposed evaluation metric and the efficiency of our algorithmic framework.

The use of probabilistic datalog programs has been recently advocated for applications that involve recursive computation and uncertainty. While using such programs allows for a flexible knowledge derivation, it makes the analysis of query results a challenging task. Particularly, given a set O of output tuples and a number k, one would like to understand which k-size subset of the input tuples have contributed the most to the derivation of O. This is useful for multiple tasks, such as identifying the critical sources of errors and understanding surprising results. Previous works have mainly focused on the quantification of tuples contribution to a query result in non-recursive SQL queries, very often disregarding probabilistic inference. To quantify the contribution in probabilistic datalog programs, one must account for the recursive relations between input and output data, and the uncertainty. To this end, we formalize the Contribution Maximization (CM) problem. We then reduce CM to the well-studied Influence Maximization (IM) problem, showing that we can harness techniques developed for IM to our setting. However, we show that such naïve adoption results in poor performance. To overcome this, we propose an optimized algorithm which injects a refined variant of the classic Magic Sets technique, integrated with a sampling method, into IM algorithms, achieving a significant saving of space and execution time. Our experiments demonstrate the effectiveness of our algorithm, even where the naïve approach is infeasible.

Search advertising is one of the most commonly-used methods of advertising. Past work has shown that search advertising can be employed to improve health by eliciting positive behavioral change. However, writing effective advertisements requires expertise and (possible expensive) experimentation, both of which may not be available to public health authorities wishing to elicit such behavioral changes, especially when dealing with a public health crises such as epidemic outbreaks.

The problem of finding an item-set of maximal aggregated utility that satisfies a set of constraints is at the cornerstone of many e-commerce applications. Its classical definition assumes that all the information needed to verify the constraints is explicitly given. In practice, however, the data available in e-commerce databases on the items is often partial. Hence, adequately answering constrained search queries requires the completion of this missing information. A common approach to complete missing data is to employ Machine Learning (ML) algorithms. However, ML is naturally error-prone. More accurate data can be obtained by asking the items' sellers to complete missing data. But as the number of items in the repository is huge, asking sellers about all items is prohibitively expensive. CONCIERGE, our presented system, assists the e-commerce platform in identifying a bounded-size set of items whose data should be manually completed, as these items are expected to contribute the most to the constrained search queries in question. We demonstrate the effectiveness of our system on real-world data and scenarios taken from a large e-commerce system by interacting with the VLDB'20 participants who act as both analysts and the sellers.

In applications with large userbases such as crowdsourcing, social networks or recommender systems, selecting users is a common and challenging task. Different applications require different policies for selecting users, and implementing such policies is applicationspecific and laborious. To this end, we introduce a novel declarative framework that abstracts common components of the user selection problem, while allowing for domain-specific tuning. The framework is based on an ontology view of user profiles, with respect to which we define a query language for policy specification. Our language extends SPARQL with means for capturing soft constraints which are essential for worker selection. At the core of our query engine is then a novel efficient algorithm for handling these constraints. Our experimental study on real-life data indicates the effectiveness and flexibility of our approach, showing in particular that it outperforms existing task-specific solutions in prominent user selection tasks.

The use of probabilistic datalog programs has been advocated for applications that involve recursive computation and uncertainty. While using such programs allows for a flexible knowledge derivation, it makes the analysis of query results a challenging task. Particularly, given a set O of output tuples and a number k, one would like to understand which k-size subset of the input tuples has affected the most the derivation of O. This is useful for multiple tasks, such as identifying critical sources of errors and understanding surprising results. To this end, we formalize the Contribution Maximization problem and present an efficient algorithm to solve it. Our algorithm injects a refined variant of the classic Magic Sets technique, integrated with a sampling method, into top-performing algorithms for the well-studied Influence Maximization problem. We propose to demonstrate our solution in a system called PODIUM. We will demonstrate the usefulness of PODIUM using real-life data and programs, and illustrate the effectiveness of our algorithm.

As more and more social network users interact through Internet Memes, an emerging popular type of captioned images, there is a growing need for users to quickly retrieve the right Meme for a given situation. As opposed conventional image search, visually similar Memes may reflect different concepts. Intent is sometimes captured by user annotations (e.g., tags), but these are often incomplete and ambiguous. Thus, a deeper analysis of the relations among Memes is required for an accurate, custom search. To address this problem, we present SimMeme, a Meme-dedicated search engine. SimMeme uses a generic graph-based data model that aligns various types of information about the Memes with a semantic ontology. A novel similarity measure that effectively considers all incorporated data is employed and serves as the foundation of our system. Our experimental results achieve using common evaluation metrics and crowd feedback, over a large repository of real-life annotated Memes, show that in the task of Meme retrieval, SimMeme outperforms state-of-the-art solutions for image retrieval.

Parkinson's disease (PD) is a slowly progressing neurodegenerative disease with early manifestation of motor signs. Recently, there has been a growing interest in developing automatic tools that can assess motor function in PD patients. Here we show that mouse tracking data collected during people's interaction with a search engine can be used to distinguish PD patients from similar, non-diseased users and present a methodology developed for the diagnosis of PD from these data. The main challenge we address is the extraction of informative features from raw mouse tracking data. We do so in two complementary ways: First, we manually construct expert-recommended features, aiming to identify abnormalities in motor behaviors. Second, we use an unsupervised representation learning technique to map these raw data to high-level features. Using all the extracted features, a Random Forest classifier is then used to distinguish PD patients from controls, achieving an AUC of 0.92, while results using only expert-generated or auto-generated features are 0.87 and 0.83, respectively. Our results indicate that mouse tracking data can help in detecting users at early stages of the disease and that both expert-generated features and unsupervised techniques for feature generation are required to achieve the best possible performance.

Influence Maximization (IM) is the problem of finding a set of influential users in a social network, so that their aggregated influence is maximized. IM has natural applications in viral marketing and has been the focus of extensive recent research. One critical problem, however, is that while existing IM algorithms serve the goal of reaching a large audience, they may obliviously focus on certain well-connected populations, at the expense of key demographics, creating an undesirable imbalance, an illustration of a broad phenomenon referred to as algorithmic discrimination. Indeed, we demonstrate an inherent trade-off between two objectives: (1) maximizing the overall influence and (2) maximizing influence over a predefined "protected" demographic, with the optimal balance between the two being open to different interpretations. To this end, we present IM-Balanced, a system enabling end users to declaratively specify the desired trade-off between these objectives w.r.t. an emphasized population. IM-Balanced provides theoretical guarantees for the proximity to the optimal solution in terms of both objectives and ensures an efficient, scalable computation via careful adaptation of existing state-of-the-art IM algorithms. Our demonstration illustrates the effectiveness of our approach through real-life viral marketing scenarios in an academic social network.

People seeking information through search engines are assumed to behave similarly, regardless of the topic which they are searching. Here we use mouse tracking, which is correlated with gaze, to show that the information seeking patterns of people differ dramatically depending on their level of anxiety at the time of the search. We investigate the behavior of people during searches for medical symptoms, ranging from benign indications, where users are not usually anxious, to ones which could harbinger life-threatening conditions, where extreme anxiety is expected. We show that for the latter, 90% of people never saw more than the top 67% of the screen, compared to over 95% scanned by people seeking information on benign symptoms, even though relevant documents are similarly distributed in the results pages to these queries. Based on this observation, we develop a model which can predict the level of anxiety experienced by a user, using attributes derived from mouse tracking data and other user interactions. The model achieves Kendall's Tau of 0.48 with the medical severity of the symptoms searched. We show the importance of using information about the users? level of anxiety as predicted by the model, when measuring search engine performance. Our results prove that ignoring this information can lead to significant over-estimation of performance. Additionally, we show the utility of the model in three special instances: where multiple symptoms are searched concurrently; where the searcher has an underlying medical condition; and when users seek information on ways to commit suicide. In the latter, our results demonstrate the importance of help-line notices, and emphasize the need to measure the effective number of results seen by the user. Our results indicate that measures of relevance which use anxiety information can lead to more accurate understanding of the quality of search results, especially when delivering potentially life-saving information to users.

With the proliferation of social image-sharing applications, image search becomes an increasingly common activity. In this work, we focus on a particular class of images that convey semantic meaning beyond the visual appearance, and whose search presents particular challenges. A prominent example is Memes, an emerging popular type of captioned pictures, which we will use in this demo to demonstrate our solution. Unlike in conventional image-search, visually similar Memes may reflect different concepts. The intent is sometimes captured by user annotations, but these too are often incomplete and ambiguous. Thus, a deeper analysis of the semantic relations among Memes is required for an accurate search. To address this problem, we present SimMeme, a semantic aware search engine for Memes. SimMeme uses a generic graph-based data model that aligns all the information available about the Memes with a semantic ontology. A novel similarity measure that interweaves common image, textual, structural and semantic similarities into one holistic measure is employed to effectively answer user queries. We will demonstrate the operation of SimMeme over a large repository of real-life annotated Memes which we have constructed by web crawling and crowd annotations, allowing users to appreciate the quality of the search results as well as the execution efficiency.

Adequate crowd selection is an important factor in the success of crowdsourcing platforms, increasing the quality and relevance of crowd answers and their performance in different tasks. The optimal crowd selection can greatly vary depending on properties of the crowd and of the task. To this end, we present December, a declarative platform with novel capabilities for flexible crowd selection. December supports the personalized selection of crowd members via a dedicated query language Member-QL. This language enables specifying and combining common crowd selection criteria such as properties of a crowd member's profile and history, similarity between profiles in specific aspects and relevance of the member to a given task. This holistic, customizable approach differs from previous work that has mostly focused on dedicated algorithms for crowd selection in specific settings. To allow efficient query execution, we implement novel algorithms in December based on our generic, semantically-aware definitions of crowd member similarity and expertise. We demonstrate the effectiveness of December and Member-QL by using the VLDB community as crowd members and allowing conference participants to choose from among these members for different purposes and in different contexts.