Modern companies and institutions rely on data to guide every single decision. Missing or incorrect information seriously compromises any decision process. We demonstrate "Deequ", an Apache Spark-based library for automating the verification of data quality at scale. This library provides a declarative API, which combines common quality constraints with user-defined validation code, and thereby enables "unit tests for data". Deequ is available as open source, meets the requirements of production use cases at Amazon, and scales to datasets with billions of records if the constraints to evaluate are chosen carefully. Our demonstration walks attendees through a fictitious business use case of validating daily product reviews from a public dataset, and is executed in a proprietary interactive notebook environment. We show attendees how to define data unit tests from automatically suggested constraints and how to create customized tests. Additionally, we demonstrate how to apply Deequ to validate incrementally growing datasets, and give examples of how to configure anomaly detection algorithms on time series of data quality metrics to further automate the data validation.

The DEEM workshop brings together researchers and practitioners at the intersection of applied machine learning, data management and systems research, with the goal to discuss the arising data management issues in machine learning application scenarios.

Analyzing and explaining relationships between entities in a knowledge graph is a fundamental problem with many applications. Prior work has been limited to extracting the most informative subgraph connecting two entities of interest. This paper extends and generalizes the state of the art by considering the relationships between two sets of entities given at query time. Our method, coined ESPRESSO, explains the connection between these sets in terms of a small number of relatedness cores: dense sub-graphs that have strong relations with both query sets. The intuition for this model is that the cores correspond to key events in which entities from both sets play a major role. For example, to explain the relationships between US politicians and European politicians, our method identifies events like the PRISM scandal and the Syrian Civil War as relatedness cores. Computing cores of bounded size is NP-hard. This paper presents efficient approximation algorithms. Our experiments with real-life knowledge graphs demonstrate the practical viability of our approach and, through user studies, the superior output quality compared to state-of-the-art baselines.

We investigate a new approach to the design of distributed, shared-nothing RDF engines. Our engine, coined "TriAD", combines join-ahead pruning via a novel form of RDF graph summarization with a locality-based, horizontal partitioning of RDF triples into a grid-like, distributed index structure. The multi-threaded and distributed execution of joins in TriAD is facilitated by an asynchronous Message Passing protocol which allows us to run multiple join operators along a query plan in a fully parallel, asynchronous fashion. We believe that our architecture provides a so far unique approach to join-ahead pruning in a distributed environment, as the more classical form of sideways information passing would not permit for executing distributed joins in an asynchronous way. Our experiments over the LUBM, BTC and WSDTS benchmarks demonstrate that TriAD consistently outperforms centralized RDF engines by up to two orders of magnitude, while gaining a factor of more than three compared to the currently fastest, distributed engines. To our knowledge, we are thus able to report the so far fastest query response times for the above benchmarks using a mid-range server and regular Ethernet setup.

In this paper, we propose a scalable and highly efficient index structure for the reachability problem over graphs. We build on the well-known node interval labeling scheme where the set of vertices reachable from a particular node is compactly encoded as a collection of node identifier ranges. We impose an explicit bound on the size of the index and flexibly assign approximate reachability ranges to nodes of the graph such that the number of index probes to answer a query is minimized. The resulting tunable index structure generates a better range labeling if the space budget is increased, thus providing a direct control over the trade off between index size and the query processing performance. By using a fast recursive querying method in conjunction with our index structure, we show that, in practice, reachability queries can be answered in the order of microseconds on an off-the-shelf computer - even for the case of massive-scale real world graphs. Our claims are supported by an extensive set of experimental results using a multitude of benchmark and real-world web-scale graph datasets.

Measuring the semantic relatedness between two entities is the basis for numerous tasks in IR, NLP, and Web-based knowledge extraction. This paper focuses on disambiguating names in a Web or text document by jointly mapping all names onto semantically related entities registered in a knowledge base. To this end, we have developed a novel notion of semantic relatedness between two entities represented as sets of weighted (multi-word) keyphrases, with consideration of partially overlapping phrases. This measure improves the quality of prior link-based models, and also eliminates the need for (usually Wikipedia-centric) explicit interlinkage between entities. Thus, our method is more versatile and can cope with long-tail and newly emerging entities that have few or no links associated with them. For efficiency, we have developed approximation techniques based on min-hash sketches and locality-sensitive hashing. Our experiments on semantic relatedness and on named entity disambiguation demonstrate the superiority of our method compared to state-of-the-art baselines.

Computing shortest paths between two given nodes is a fundamental operation over graphs, but known to be nontrivial over large disk-resident instances of graph data. While a number of techniques exist for answering reachability queries and approximating node distances efficiently, determining actual shortest paths (i.e. the sequence of nodes involved) is often neglected. However, in applications arising in massive online social networks, biological networks, and knowledge graphs it is often essential to find out many, if not all, shortest paths between two given nodes.

Graphs are increasingly used to model a variety of loosely structured data such as biological or social networks and entity-relationships. Given this profusion of large-scale graph data, efficiently discovering interesting substructures buried within is essential. These substructures are typically used in determining subsequent actions, such as conducting visual analytics by humans or designing expensive biomedical experiments. In such settings, it is often desirable to constrain the size of the discovered results in order to directly control the associated costs. In this paper, we address the problem of finding cardinality-constrained connected sub trees in large node-weighted graphs that maximize the sum of weights of selected nodes. We provide an efficient constant-factor approximation algorithm for this strongly NP-hard problem. Our techniques can be applied in a wide variety of application settings, for example in differential analysis of graphs, a problem that frequently arises in bioinformatics but also has applications on the web.

We study how to automatically extract tourist trips from large volumes of geo-tagged photographs. Working with more than 8 million of these photographs that are publicly available via photo- sharing communities such as Flickr and Panoramio, our goal is to satisfy the needs of a tourist who specifies a starting location (typically a hotel) together with a bounded travel distance and demands a tour that visits the popular sites along the way. Our system, named ANTOURAGE, solves this intractable problem using a novel adaptation of the max-min ant system (MMAS) meta-heuristic. Experiments using GPS metadata crawled from Flickr show that ANTOURAGE can generate high-quality tours.