Search engines can quickly response a hyperlink list according to query keywords. However, when a query is complex, developers need to repeatedly refine the search keywords and open a large number of web pages to find and summarize answers. Many research works of question and answering (Q and A) system attempt to assist search engines by providing simple, accurate and understandable answers. However, without original semantic contexts, these answers lack explainability, making them difficult for users to trust and adopt. In this paper, a brain-inspired search engine assistant named DeveloperBot based on knowledge graph is proposed, which aligns to the cognitive process of human and has the capacity to answer complex queries with explainability. Specifically, DeveloperBot firstly constructs a multi-layer query graph by splitting a complex multi-constraint query into several ordered constraints. Then it models the constraint reasoning process as subgraph search process inspired by the spreading activation model of cognitive science. In the end, novel features of the subgraph will be extracted for decision-making. The corresponding reasoning subgraph and answer confidence will be derived as explanations. The results of the decision-making demonstrate that DeveloperBot can estimate the answers and answer confidences with high accuracy. We implement a prototype and conduct a user study to evaluate whether and how the direct answers and the explanations provided by DeveloperBot can assist developers' information needs.

Query optimizers rely on accurate cardinality estimation (CardEst) to produce good execution plans. The core problem of CardEst is how to model the rich joint distribution of attributes in an accurate and compact manner. Despite decades of research, existing methods either over simplify the models only using independent factorization which leads to inaccurate estimates and sub optimal query plans, or over-complicate them by lossless conditional factorization without any independent assumption which results in slow probability computation. In this paper, we propose FLAT, a CardEst method that is simultaneously fast in probability computation, lightweight in model size and accurate in estimation quality. The key idea of FLAT is a novel unsupervised graphical model, called FSPN. It utilizes both independent and conditional factorization to adaptively model different levels of attributes correlations, and thus subsumes all existing CardEst models and dovetails their advantages. FLAT supports efficient online probability computation in near liner time on the underlying FSPN model, and provides effective offline model construction. It can estimate cardinality for both single table queries and multi-table join queries. Extensive experimental study demonstrates the superiority of FLAT over existing CardEst methods on well-known benchmarks: FLAT achieves 1 to 5 orders of magnitude better accuracy, 1 to 3 orders of magnitude faster probability computation speed (around 0.2ms) and 1 to 2 orders of magnitude lower storage cost (only tens of KB).

Noisy pairwise comparison feedback has been incorporated to improve the overall query complexity of interactively learning binary classifiers. The \textit{positivity comparison oracle} is used to provide feedback on which is more likely to be positive given a pair of data points. Because it is impossible to infer accurate labels using this oracle alone \textit{without knowing the classification threshold}, existing methods still rely on the traditional \textit{explicit labeling oracle}, which directly answers the label given a data point. Existing methods conduct sorting on all data points and use explicit labeling oracle to find the classification threshold. The current methods, however, have two drawbacks: (1) they needs unnecessary sorting for label inference; (2) quick sort is naively adapted to noisy feedback and negatively affects practical performance. In order to avoid this inefficiency and acquire information of the classification threshold, we propose a new pairwise comparison oracle concerning uncertainties. This oracle receives two data points as input and answers which one has higher uncertainty. We then propose an efficient adaptive labeling algorithm using the proposed oracle and the positivity comparison oracle. In addition, we also address the situation where the labeling budget is insufficient compared to the dataset size, which can be dealt with by plugging the proposed algorithm into an active learning algorithm. Furthermore, we confirm the feasibility of the proposed oracle and the performance of the proposed algorithm theoretically and empirically.

Motivated by the mode estimation problem of an unknown multivariate probability density function, we study the problem of identifying the point with the minimum k-th nearest neighbor distance for a given dataset of n points. We study the case where the pairwise distances are apriori unknown, but we have access to an oracle which we can query to get noisy information about the distance between any pair of points. For two natural oracle models, we design a sequential learning algorithm, based on the idea of confidence intervals, which adaptively decides which queries to send to the oracle and is able to correctly solve the problem with high probability. We derive instance-dependent upper bounds on the query complexity of our proposed scheme and also demonstrate significant improvement over the performance of other baselines via extensive numerical evaluations.

Question Answering (QA) on Electronic Health Records (EHR), namely EHR QA, can work as a crucial milestone towards developing an intelligent agent in healthcare. EHR data are typically stored in a relational database, which can also be converted to a Directed Acyclic Graph (DAG), allowing two approaches for EHR QA: Table-based QA and Knowledge Graph-based QA. We hypothesize that the graph-based approach is more suitable for EHR QA as graphs can represent relations between entities and values more naturally compared to tables, which essentially require JOIN operations. To validate our hypothesis, we first construct EHR QA datasets based on MIMIC-III, where the same question-answer pairs are represented in SQL (table-based) and SPARQL (graph-based), respectively. We then test a state-of-the-art EHR QA model on both datasets where the model demonstrated superior QA performance on the SPARQL version. Finally, we open-source both MIMICSQL* and MIMIC-SPARQL* to encourage further EHR QA research in both direction

Mastering Presto: Hands-On Learning Learn Presto - distributed SQL Query Engine for Big Data! Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes. Presto was designed and written from the ground up for interactive analytics and approaches the speed of commercial data warehouses while scaling to the size of organisations like Facebook. In the first part of the course I will talk about Presto's theory including Presto's architecture and components - coordinator, worker, connector, query execution model, etc. Additionally, I will explain to you how Kafka, Cassandra, Hive, PostgreSQL and Redshift work before I mention the specifics to their connectors.

The decision boundary of a learning algorithm applied to a given task can be viewed as the outcome of a random process: (i) generate a training set and, (ii) apply to it the, potentially randomized, learning algorithm. Recall, see Definitions 4 and 5, that a query-bounded adversary does not know the sample on which the model was trained nor the randomness used by the learner. This means that if the decision boundary has high entropy then the adversary needs to ask many questions to recover the boundary to a high degree of precision. This suggest that high-entropy decision boundaries are robust against query-bounded adversaries since intuitively it is clear that an approximate knowledge of the decision boundary is a prerequisite for a successful attack. Following this reasoning, we present two instances where high entropy of the decision boundary leads to security.

Based on Semantic Web technologies, knowledge graphs help users to discover information of interest by using live SPARQL services. Answer-seekers often examine intermediate results iteratively and modify SPARQL queries repeatedly in a search session. In this context, understanding user behaviors is critical for effective intention prediction and query optimization. However, these behaviors have not yet been researched systematically at the SPARQL session level. This paper reveals the secrets of session-level user search behaviors by conducting a comprehensive investigation over massive real-world SPARQL query logs. In particular, we thoroughly assess query changes made by users w.r.t. structural and data-driven features of SPARQL queries. To illustrate the potentiality of our findings, we employ a proof-of-concept model to predict user intentions, i.e., future directions of the given session, and give reformulation suggestions based on the predicted intention. We hope the results presented here will help to devise efficient SPARQL caching, auto-completion, query suggestion, approximation, and relaxation techniques in the future.

To cope with the intractability of answering Conjunctive Queries (CQs) and solving Constraint Satisfaction Problems (CSPs), several notions of hypergraph decompositions have been proposed -- giving rise to different notions of width, noticeably, plain, generalized, and fractional hypertree width (hw, ghw, and fhw). Given the increasing interest in using such decomposition methods in practice, a publicly accessible repository of decomposition software, as well as a large set of benchmarks, and a web-accessible workbench for inserting, analyzing, and retrieving hypergraphs are called for. We address this need by providing (i) concrete implementations of hypergraph decompositions (including new practical algorithms), (ii) a new, comprehensive benchmark of hypergraphs stemming from disparate CQ and CSP collections, and (iii) HyperBench, our new web-inter\-face for accessing the benchmark and the results of our analyses. In addition, we describe a number of actual experiments we carried out with this new infrastructure.

Existing tools for Question Answering (QA) have challenges that limit their use in practice. They can be complex to set up or integrate with existing infrastructure, do not offer configurable interactive interfaces, and do not cover the full set of subtasks that frequently comprise the QA pipeline (query expansion, retrieval, reading, and explanation/sensemaking). To help address these issues, we introduce NeuralQA - a usable library for QA on large datasets. NeuralQA integrates well with existing infrastructure (e.g., ElasticSearch instances and reader models trained with the HuggingFace Transformers API) and offers helpful defaults for QA subtasks. It introduces and implements contextual query expansion (CQE) using a masked language model (MLM) as well as relevant snippets (RelSnip) - a method for condensing large documents into smaller passages that can be speedily processed by a document reader model. Finally, it offers a flexible user interface to support workflows for research explorations (e.g., visualization of gradient-based explanations to support qualitative inspection of model behaviour) and large scale search deployment. Code and documentation for NeuralQA is available as open source on Github.