We argue for a substantial shift away from human-crafted solutions and towards leveraging machine learning algorithms to address the above challenges. These algorithms can be trained on application- specific properties and customized performance goals to automatically learn how to provision resources as well as schedule the execution of incoming query workloads. Towards this vision, we have developed WiSeDB, a learning-based performance management service for cloud-deployed data management applications. In this talk, I will discuss how WiSeDB uses (a) supervised learning to automatically learn cost-effective models for guiding query placement, scheduling, and resource provisioning decisions for batch workload processing, and (b) reinforcement learning to naturally adapt to changes in query arrival rates and dynamic resource availability, while being decoupled from notoriously inaccurate performance prediction models.

Approximate lifted inference with probabilistic databases

Performing inference over large uncertain data sets is becoming a central data management problem. Recent large knowledge bases, such as Yago, Nell or DeepDive, have millions to billions of uncertain tuples. Because general reasoning under uncertainty is highly intractable, many state-of-the-art systems today perform approximate inference by reverting to sampling. This talk shows an alternative approach that allows approximate ranking answers to hard probabilistic queries in guaranteed polynomial time, and by using only basic operators of existing database management systems (i.e. no sampling required).

(1) The first part of this talk develops upper and lower bounds for the probability of Boolean functions by treating multiple occurrences of variables as independent and assigning them new individual probabilities. We call this approach dissociation and give an exact characterization of optimal oblivious bounds (i.e. when the new probabilities are chosen independent of the probabilities of all other variables). Our new bounds shed light on the connection between previous relaxation-based and model-based approximations and unify them as concrete choices in a larger design space.

(2) The second part then draws the connection to lifted inference and shows how the problem of approximate probabilistic inference can be entirely reduced to a standard query evaluation problem with aggregates. There are no iterations and no exponential blow-ups. All benefits of relational engines (such as cost-based optimizations, multi-core query processing, shared-nothing parallelization) are directly available to queries over probabilistic databases. To achieve this, we compute approximate rather than exact probabilities, with a one-sided guarantee: The probabilities are guaranteed to be upper bounds to the true probabilities, which we show is sufficient to rank the top query answers with high precision. We give experimental evidence on synthetic TPC-H data that this approach can be orders of magnitude faster and also more accurate than sampling-based approaches.

(Based on joint work with Dan Suciu from TODS 2014, VLDB 2015, and VLDBJ 2016: http://arxiv.org/pdf/1409.6052, http://arxiv.org/pdf/1412.1069, http://arxiv.org/pdf/1310.6257)

For several decades now, the amount of data available to us has been growing at a pace far higher than our ability to process it; this trend, popularly referred to as "big data", has accelerated many-fold in recent years with the emergence of efficient and mass-produced scientific instruments, increasing ease of generating and publishing data, and proliferation of Internet-connected devices. In this talk, I will present an overview of two recent projects from my group at UMD on building scalable platforms for large-scale data analytics.

First, I will discuss our ongoing work on building a platform, called "DataHub", for enabling collaborative data science, where teams of data scientists can simultaneously analyze, modify, and share datasets, to understand trends and to extract actionable insights. While numerous solutions exist for specific data analysis tasks, underlying infrastructure and data management capabilities for supporting ad hoc collaboration pipelines are still largely missing. I will present our vision for a unified, dataset-centric platform for addressing these challenges, and present our recent work on: (a) efficiently managing a large number versioned datasets, (b) designing and supporting a unified query language to seamlessly query versioning and provenance information, and (c) lifecycle management of complex machine learning models like deep neural networks.

Second, I will present our initial work on extracting hidden graphs from relational databases. Although there has been much work on large-scale graph analytics, graphs are not the primary representation choice for most data today, and users who want to employ graph analytics are forced to extract data from their data stores, construct the requisite graphs, and then use a specialized engine to write and execute their graph analysis tasks. I will describe our work on a system called GraphGen, that enables users to declaratively specify graph extraction tasks over relational databases, visually explore the extracted graphs, and write and execute graph algorithms over them, either directly or using existing graph libraries like the widely used NetworkX Python library.

A Hitchhiker's Guide to Ontology

In this talk, I will give an overview of our recent work in the area of knowledge bases. I will first talk about our main project, the YAGO knowledge base. YAGO is now multilingual, and has grown into a larger project at the Max Planck Institute for Informatics and Télécom ParisTech. I will then talk about rule mining. We can find semantic correlations in the form of Horn rules in the knowledge base. In our newest work, we show how rule mining can be applied to predict the completeness or incompleteness of the data in the knowledge base. I will also talk about watermarking approaches to trace the provenance of ontological data. Finally, I will showcase our work on creativity in knowledge bases.

Data Profiling

Data profiling deserves a fresh look for two reasons: First, the area itself is neither established nor defined in any principled way, despite significant research activity on individual parts in the past. Second, current data profiling techniques hardly scale beyond what can only be called small data. Finally, more and more data beyond the traditional relational databases are being created and beg to be profiled. The talk highlights the state of the art and proposes new research directions and challenges.

This work partially funded by the European Commission under the Integrated Project CoherentPaaS.

While there is no standard in the blockchain space currently, all the ongoing efforts involve some combination of database, transaction, encryption, consensus and other distributed systems technologies. Some of the application areas in which blockchain pilots are being carried out are: smart contracts, supply chain management, know your customer, derivatives processing and provenance management. In this talk, I will survey some of the ongoing blockchain projects with respect to their architectures in general and their approaches to some specific technical areas. I will focus on how the functionality of traditional and modern data stores are being utilized or not utilized in the different blockchain projects. I will also distinguish how traditional distributed database management systems have handled replication and how blockchain systems do it. Since most of the blockchain efforts are still in a nascent state, the time is right for database and other distributed systems researchers and practitioners to get more deeply involved to focus on the numerous open problems.