Detecting changes in multidimensional data streams is an important and challenging task. In unsupervised change detection, changes are usually detected by comparing the distribution in a current (test) window with a reference window. It is thus essential to design divergence metrics and density estimators for comparing the data distributions, which are mostly done for univariate data. Detecting changes in multidimensional data streams brings difficulties to the density estimation and comparisons. In this paper, we propose a framework for detecting changes in multidimensional data streams based on principal component analysis, which is used for projecting data into a lower dimensional space, thus facilitating density estimation and change-score calculations. The proposed framework also has advantages over existing approaches by reducing computational costs with an efficient density estimator, promoting the change-score calculation by introducing effective divergence metrics, and by minimizing the efforts required from users on the threshold parameter setting by using the Page-Hinkley test. The evaluation results on synthetic and real data show that our framework outperforms two baseline methods in terms of both detection accuracy and computational costs.

Employees that spend more time finding relevant data than analyzing it suffer a data discovery problem. The large volume of data in enterprises, and sometimes the lack of knowledge of the schemas aggravates this problem. Similar to how we navigate the Web today, we propose to identify semantic links that assist analysts in their discovery tasks. These links relate tables to each other, to facilitate navigating the schemas. They also relate data to external data sources such as ontologies and dictionaries, to help explain the schema meaning. We materialize the links in an enterprise knowledge graph, where they become available to analysts. The main challenge is how to find pairs of objects that are semantically related. We propose SEMPROP, a DAG of different components that find links based on syntactic and semantic similarities. SEMPROP is commanded by a semantic matcher which leverages word embeddings to find objects that are semantically related. To leverage word embeddings, we introduce coherent groups, a novel technique to combine them which works better than other state of the art alternatives for this problem. We implement SEMPROP as part of a discovery system we are building and conduct user studies, real deployments and a quantitative evaluation to understand the benefits of links for data discovery tasks, as well as the benefits of SEMPROP and coherent groups to find those links.

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Finding relevant data for a specific task from the numerous data sources available in any organization is a daunting task. This is not only because of the number of possible data sources where the data of interest resides, but also due to the data being scattered all over the enterprise and being typically dirty and inconsistent. In practice, data scientists are routinely reporting that the majority (more than 80%) of their e↵ort is spent finding, cleaning, integrating, and accessing data of interest to a task at hand. We propose to demonstrate Data Civilizer to ease the pain faced in analyzing data "in the wild". Data Civilizer is an end-to-end big data management system with components for data discovery, data integration and stitching, data cleaning, and querying data from a large variety of storage engines, running in large enterprises.

Patterns (or regex-based expressions) are widely used to constrain the format of a domain (or a column), e.g., a Year column should contain only four digits, and thus a value like "1980-" might be a typo. Moreover, integrity constraints (ICs) defined over multiple columns, such as (conditional) functional dependencies and denial constraints, e.g., a ZIP code uniquely determines a city in the UK, have been widely used in data cleaning. However, a promising, but not yet explored, direction is to combine regex-and IC-based theories to capture data dependencies involving partial attribute values. For example, in an employee ID such as"F-9-107", "F" is sufficient to determine the finance department. Inspired by the above observation, we propose a novel class of ICs, called pattern functional dependencies (PFDs), to model fine-grained data dependencies gleaned from partial attribute values. These dependencies cannot be modeled using traditional ICs, such as (conditional) functional dependencies, which work on entire attribute values. We also present a set of axioms for the inference of PFDs, analogous to Armstrong's axioms for FDs, and study the complexity of consistency and implication analysis of PFDs. Moreover, we devise an effective algorithm to automatically discover PFDs even in the presence of errors in the data. Our extensive experiments on 15 real-world datasets show that our approach can effectively discover valid and useful PFDs over dirty data, which can then be used to detect data errors that are hard to capture by other types of ICs.