DATA Lab seminar: group meetings & guest speakers

We are currently meeting Wednesdays and/or Fridays at noon in WVH 462 (map) for brown bag lunch. Please subscribe to our DATA Lab talks email list or DATA Lab talks calendar if you think data is the future.

Fall 2018

  • (9/13, 10:40am): WVH 366: Anshumali Shrivastava (Rice University): Hashing Algorithms for Extreme Scale Machine Learning details)

    Hashing Algorithms for Extreme Scale Machine Learning.

    In this talk, I will discuss some of my recent and surprising findings on the use of hashing algorithms for large-scale estimations. Locality Sensitive Hashing (LSH) is a hugely popular algorithm for sub-linear near neighbor search. However, it turns out that fundamentally LSH is a constant time (amortized) adaptive sampler from which efficient near-neighbor search is one of the many possibilities. Our observation adds another feather in the cap for LSH. LSH offers a unique capability to do smart sampling and statistical estimations at the cost of few hash lookups. Our observation bridges data structures (probabilistic hash tables) with efficient unbiased statistical estimations. I will demonstrate how this dynamic and efficient sampling beak the computational barriers in adaptive estimations where, for the first time, it is possible that we pay roughly the cost of uniform sampling but get the benefits of adaptive sampling. We will demonstrate the power of one simple idea for three favorite problems 1) Partition function estimation for large NLP models such as word2vec, 2) Adaptive Gradient Estimations for efficient SGD and 3) Sub-Linear Deep Learning with Huge Parameter Space.

    In the end, if time permits, we will switch to memory cost show a simple hashing algorithm that can shrink memory requirements associated with classification problems exponentially! Using our algorithms, we can train 100,000 classes with 400,000 features, on a single Titan X while only needing 5% or less memory required to store all the weights. Running a simple logistic regression on this data, the model size of 320GB is unavoidable.

    Anshumali Shrivastava is an assistant professor in the computer science department at Rice University. His broad research interests include randomized algorithms for large-scale machine learning. He is a recipient of National Science Foundation (NSF) CAREER Award, a Young Investigator Award from Air Force Office of Scientific Research (AFOSR), and machine learning research award from Amazon. His research on hashing inner products has won Best Paper Award at NIPS 2014 while his work on representing graphs got the Best Paper Award at IEEE/ACM ASONAM 2014. Anshumali got his PhD in 2015 from Cornell University.

  • (9/26, 12:00pm): Spyros Blanas (Ohio State University): Scaling database systems to high-performance computers details)

    Scaling database systems to high-performance computers

    We are witnessing the increasing use of warehouse-scale computers to analyze massive datasets quickly. This poses two challenges for database systems. The first challenge is interoperability with established analytics libraries and tools. Massive datasets often consist of images (arrays) in file formats like FITS and HDF5. To analyze such datasets users turn to domain-specific libraries and deep learning frameworks, and thus write code that directly manipulates files. We will first present ArrayBridge, an open-source I/O library that allows SciDB, TensorFlow and HDF5-based programs to co-exist in a pipeline without converting between file formats. With ArrayBridge, users benefit from the optimizations of a database system without sacrificing the ability to directly manipulate data through the existing HDF5 API when they want to.

    The second challenge is scalability, as warehouse-scale computers expose communication bottlenecks in foundational data processing operations. This talk will focus on data shuffling and parallel aggregation. We will first present an RDMA-aware data shuffling algorithm that transmits data up to 4X faster than MPI. This is achieved by switching to a connectionless, datagram-based network transport layer that scales better but requires flow control in software. We will then present a parallel aggregation algorithm for high-cardinality aggregation that carefully schedules data transmissions to avoid unscaleable all-to-all communication. The algorithm leverages similarity to transmit less data over congested network links. We will conclude by highlighting additional challenges that need to be overcome to scale database systems to massive computers.

    Spyros Blanas is an assistant professor in the Department of Computer Science and Engineering at The Ohio State University. His research interest is high performance database systems, and his current goal is to build a database system for high-end computing facilities. He has received the IEEE TCDE Rising Star award and a Google Research Faculty award. He completed his Ph.D. at the University of Wisconsin–Madison where part of his Ph.D. dissertation was commercialized in Microsoft SQL Server as the Hekaton in-memory transaction processing engine.

    EUROSYS 2017:

    ICDE 2018:

  • (9/28, 12:00pm): Stratos Idreos (Harvard): The periodic table of data structures details)

    The periodic table of data structures.

    Data structures are critical in any data-driven scenario, and they define the behavior of modern data systems and data-driven algorithms. However, they are notoriously hard to design due to a massive design space and the dependence of performance on workload and hardware which evolve continuously.

    What if we knew how many and which data structures are possible to design? What if we could compute the expected performance of a data structure design on a given workload and hardware without having to implement it and without even having access to the target machine? We will discuss our quest for 1) the first principles of data structures, 2) design continuums that make it possible to automate design, and 3) self-designing systems that can morph between what we now consider fundamentally different structures. We will draw examples from the NoSQL key-value store design space and discuss how to accelerate them and balance space-time tradeoffs.

    Stratos Idreos is an assistant professor of Computer Science at Harvard University where he leads DASlab, the Data Systems Laboratory. Stratos was awarded the 2011 ACM SIGMOD Jim Gray Doctoral Dissertation award and the 2011 ERCIM Cor Baayen award. He is also a recipient of an IBM zEnterpise System Recognition Award, a VLDB Challenges and Visions best paper award and an NSF Career award. In 2015 he was awarded the IEEE TCDE Rising Star Award from the IEEE Technical Committee on Data Engineering for his work on adaptive data systems.  


    Prior recorded talk at UW:

    Further pointer:

  • (10/4, 4:30pm): Angelika Kimmig (Cardiff University): A Collective, Probabilistic Approach to Schema Mapping details)

    A Collective, Probabilistic Approach to Schema Mapping

    We propose a probabilistic approach to the problem of schema mapping. Our approach is declarative, scalable, and extensible. It builds upon recent results in both schema mapping and probabilistic reasoning and contributes novel techniques in both fields. We introduce the problem of mapping selection, that is, choosing the best mapping from a space of potential mappings, given both metadata constraints and a data example. As selection has to reason holistically about the inputs and the dependencies between the chosen mappings, we define a new schema mapping optimization problem which captures interactions between mappings. We then introduce Collective Mapping Discovery (CMD), our solution to this problem using state-of-the-art probabilistic reasoning techniques, which allows for inconsistencies and incompleteness. Using hundreds of realistic integration scenarios, we demonstrate that the accuracy of CMD is more than 33% above that of metadata-only approaches already for small data examples, and that CMD routinely finds perfect mappings even if a quarter of the data is inconsistent.

    Angelika Kimmig is a Lecturer at Cardiff University, UK. She obtained her Ph.D. from KU Leuven, Belgium, and was a postdoctoral fellow at KU Leuven and the University of Maryland, College Park. Her research interests include symbolic AI, reasoning under uncertainty, machine learning, logic programming, and especially combinations thereof such as probabilistic programming and statistical relational learning. She is a key contributor to the probabilistic logic programming language ProbLog.

    PAPER from ICDE 2017:

  • (10/26, 12:00pm): Mania Abdi (Northeastern): D3N: A multi-layer cache for improving big-data applications’ performance in data centers with imbalanced networks details)

    D3N: A multi-layer cache for improving big-data applications’ performance in data centers with imbalanced networks

    Caching methods for improving the performance of datalakes for throughput-bound big-data jobs assume unlimited bandwidth across the datacenter. However, most enterprise and academic datacenters grow organically and have heavily oversubscribed networks between different computer clusters. This paper describes D3N, an architecture that caches data on the access side of potential network bottlenecks and uses a multilayer approach. In the case of a two layer cache, reuse distances are dynamically computed along with cache miss costs to determine the partitioning of the cache into L1 (used for local accesses) and L2 (used for remote access, but within same access side) layers. We have implemented a prototype of D3N by modifying Ceph’s RADOS gateway. Micro and macro evaluations of the prototype demonstrate that the implementation is performant enough to saturate the (40,Gbit/s) NICs and (5 GB/s read) SSD of our caching server and deliver substantial storage bandwidth improvements for real workloads. Numerical models show that a multi-level, dynamic cache can have substantial advantages over today’s single-level caches when bandwidth is constrained.

    Mania Abdi is a PhD student at the Northeastern University Solid State Storage research group. Prior to that, she was a software engineer. She received her MSc. in Computer Engineering at the Sharif University of Technology and B.Eng. in Computer Engineering at the Amirkabir University of Technology. Mania is a computer systems researcher with a storage focus and has worked on a broad set of topics, including distribted storage, caching, data center debuging, and end-to-end tracing.

  • (11/7, 12:00pm): Laurel Orr (University of Washington): Probabilistic Database Summarization for Interactive Data Exploration details)

    Probabilistic Database Summarization for Interactive Data Exploration

    A fundamental assumption of traditional DBMSs is that the database contains all information necessary to answer a query; i.e., the database contains the entire universe of data. Many data scientists, however, do not have access to the universe of data and instead rely on samples to answer queries. These data scientists need to use tools outside of the database or alter their queries to correctly reweight and debias their samples to get a more accurate query answer. This talk presents preliminary research into building the first open world database system (OWDB) that inherently assumes relations are samples from some universe, even if the sampling mechanism is unknown. We will discuss two different approaches for building an OWDB: probabilistic modeling of the universe and sample reweighting. We then present EntropyDB, a system that takes the former approach to summarize a database, and we lastly discuss ongoing research into the later approach using Bayesian Networks. We conclude with discussing the open challenges in developing an OWDB.

    Laurel Orr is a 6th year PhD student in the Database Group at the Paul G Allen School for Computer Science and Engineering. Her research interests are data summarization, approximate query processing, data exploration, and the general development of tools and techniques to aid data scientists in their data investigation and analysis pipeline. Her current research goal is to develop a prototype open world database system that inherently treats relations a samples drawn from some larger universe of data, even when the sampling mechanism is unknown. She was a 2018 NCWIT Collegiate Award Honorable Mention and was awarded a NSF Graduate Research Fellowship in 2015.



  • (11/9, 12:00pm): Raul Castor Fernandez (MIT): Aurum: A Data Discovery System details)

    Aurum: A Data Discovery System

    Organizations store data in hundreds of different data sources, including relational databases, files, and large data lake repositories. These data sources contain valuable information and insights that can be beneficial to multiple aspects of modern data-driven organizations. However, as more data is produced, our ability to use it reduces dramatically, as no single person knows about all the existent data sources. One big challenge is to discover the data sources that are relevant to answer a particular question. Aurum is a data discovery system to answer "discovery queries" on large volumes of data. In this talk, I'll motivate the data discovery problem with use cases from different industries. I will describe Aurum's design and I will talk a bit about a new research project that aims to discover data beyond tables.

    In my research, I build high-performance and scalable systems to discover, prepare and process data. I'm a postdoctoral researcher at MIT, working with Sam Madden and Mike Stonebraker. Before, I completed my PhD at Imperial College London with Peter Pietzuch.


  • (11/14, 12:00pm): Lawson Wong (Northeastern): Abstraction in robotics details)

    Abstraction in robotics

    Robotics is a big data problem. To make sense of the physical world, perform tasks well, and generalize across environments, robots need to represent and understand the world at the "correct" level of abstraction. What "correct" should mean remains to be seen.
    In this talk, I will describe two lines of work that attempt to answer this question from very different perspectives. I will first discuss work on grounding natural language instructions to robot behavior, where we demonstrate that having the right representations can enable human-robot communication. This is an important problem for robotics, since we envision users using natural language to instruct robots to perform a wide variety of tasks. In the second half, I will discuss recent preliminary work on the theoretical foundations of state abstraction in reinforcement learning, a common framework used in robot learning problems. In particular, we view state abstraction as data compression, and apply results in information theory (rate-distortion theory) to the reinforcement learning setting.
    Time permitting, I will describe some extensions to the above work, as well as other abstraction-related problems, that I envision my group will pursue at Northeastern.

    Lawson L.S. Wong is an assistant professor in the College of Computer and Information Science at Northeastern University. His research focuses on learning, representing, and estimating knowledge about the world that an autonomous robot may find useful. Prior to Northeastern, Lawson was a postdoctoral fellow at Brown University. He completed his PhD at the Massachusetts Institute of Technology. He has received a Siebel Scholarship, AAAI Robotics Student Fellowship, and Croucher Foundation Fellowship for Postdoctoral Research.

    Sequence-to-Sequence Language Grounding of Non-Markovian Task Specifications
    Nakul Gopalan, Dilip Arumugam, Lawson L.S. Wong, Stefanie Tellex
    Robotics: Science and Systems (2018)

    Grounding Natural Language Instructions to Semantic Goal Representations for Abstraction and Generalization
    Dilip Arumugam, Siddharth Karamcheti, Nakul Gopalan, Edward C. Williams, Mina Rhee, Lawson L.S. Wong, Stefanie Tellex
    Autonomous Robots (in press; 2018)
    Extended journal version of Robotics: Science and Systems (2017) paper

    State Abstraction as Compression in Apprenticeship Learning
    David Abel, Dilip Arumugam, Kavosh Asadi, Yuu Jinnai, Michael L. Littman, Lawson L.S. Wong
    Preprint available on request (by e-mail)
    To appear in AAAI Conference on Artificial Intelligence (2019)

  • (11/16, 12:00pm): Alexandra Meliou (UMass Amherst): Creating a Higher-Quality Data World details)

    Diagnoses and Explanations: Creating a Higher-Quality Data World

    The correctness and proper function of data-driven systems and applications relies heavily on the correctness of their data. Low quality data can be costly and disruptive, leading to revenue loss, incorrect conclusions, and misguided policy decisions. Improving data quality is far more than purging datasets of errors; it is critical to improve the processes that produce the data, to collect good data sources for generating the data, and to address the root causes of problems.

    Our work is grounded on an important insight: While existing data cleaning techniques can be effective at purging datasets of errors, they disregard the fact that a lot of errors are systemic, inherent to the process that produces the data, and thus will keep occurring unless the problem is corrected at its source. In contrast to traditional data cleaning, we focus on data diagnosis: explaining where and how the errors happen in a data generative process. I will describe our work on Data X-Ray and QFix, two diagnostic frameworks for large-scale extraction systems and relational data systems. I will also provide a brief overview of new results on knowledge augmentation and explanations for dataset differences, building towards a vision for toolsets that assist the exploration of information in a varied, diverse, and highly non-integrated data world.

    Alexandra Meliou is an Assistant Professor in the College of Information and Computer Sciences, at the University of Massachusetts, Amherst. Prior to that, she was a Post-Doctoral Research Associate at the University of Washington. Alexandra received her PhD degree from the Electrical Engineering and Computer Sciences Department at the University of California, Berkeley. She has received recognitions for research and teaching, including a CACM Research Highlight, an ACM SIGMOD Research Highlight Award, an ACM SIGSOFT Distinguished Paper Award, an NSF CAREER Award, a Google Faculty Research Award, and a Lilly Fellowship for Teaching Excellence. Her research focuses on data provenance, causality, explanations, data quality, and algorithmic fairness.


  • (12/7, 11:30am): Fei Chiang (McMaster University) details)

    Contextual and Spatio-temporal Data Cleaning 
    It is becoming increasingly difficult for organizations to reap value from their data due to poor data quality. This is motivated by the observation that real data is rarely error free, containing incomplete, inconsistent, and stale values. This leads to inaccurate, and out-of-date data analysis downstream. Addressing data inconsistency requires not only reconciling differing syntactic references to an entity, but it is often necessary to include domain expertise to correctly interpret the data. For example, understanding that a reference to ‘jaguar’ may be interpreted as an animal or as a vehicle. Secondly, having up-to-date (or current) data is important for timely data analysis. Cleaning stale values goes beyond just relying on timestamps, especially when timestamps may be missing, inaccurate or incomplete. 
    In this talk, I will present our work towards achieving consistent and up-to-date data. First, I will discuss contextual data cleaning that uses a new class of data integrity constraints that tightly integrate domain semantics from an ontology. Second, we argue that data currency is a relative notion based on individual spatio-temporal update patterns, and these patterns can be learned and predicted. I will present our framework to achieve these two objectives, and provide a brief overview of recent extensions with applications to knowledge fusion.
    Fei Chiang is an Assistant Professor in the Department of Computing and Software at McMaster University. She is a Faculty Fellow at the IBM Centre for Advanced Studies, and served as an inaugural Associate Director of the McMaster MacData Institute. She received her M. Math from the University of Waterloo, and B.Sc and PhD degrees from the University of Toronto, all in Computer Science. Her research interests are in data quality, data cleaning, data privacy and text mining. She holds four patents for her work in self-managing database systems. Her work has been featured in the Southern Ontario Smart Computing Impact Report. She is a recipient of the Dean’s Teaching Honour Roll, and a 2018 Ontario Early Researcher Award.

  • (12/12, 12:00pm): Erkang Zhu (University of Toronto) details)

    Title: Get Your Data Together! Algorithms for Managing Data Lakes


    Data lakes (e.g., enterprise data catalogs and Open Data portals) are data dumps if users cannot find and utilize the data in them. In this talk, I present two problems in massive, dynamic data lakes: 1) searching for joinable tables without a precomputed join graph, and 2) joining tables from different sources through auto-generated syntactic transformation on join values. I will also present two algorithmic solutions that can be used for data lakes that are large both in the number of tables (millions) and table sizes. The presented work has been published in SIGMOD and VLDB.



    Erkang (Eric) Zhu is a 5th year computer science PhD candidate at University of Toronto. His supervisor is Prof. Renée J. Miller. His research focuses on data discovery, large-scale similarity search, and randomized algorithms (data sketches).


Spring 2019

  • (1/9, 12:00pm): Kathleen Fisher (Tufts) details)

  • (1/16, 12:00pm): Evimaria Terzi (BU) details)

  • (1/18, 12:00pm): Olga Papaemmanouil (Brandeis University): Deep Learning meets Query Optimization details)

    Deep Learning meets Query Optimization

    Query optimization remains one of the most important and well studied problems in database systems. However, traditional query optimizers are complex, heuristically-driven systems that do not to learn from past experiences: they plan the execution of a query, but are ignorant of the actual performance of the picked plan. Because of the lack of feedback, a query optimizer may select the same bad query plan repeatedly, never learning from its previous good or bad choices.

    In this talk, I will argue that a new type of query optimizer, one that integrates deep learning with query optimization, can drastically improve on the state-of-the-art. Towards this direction, I will discuss ReJOIN, a proof-of-concept join enumerator that relies on deep reinforcement learning. ReJOIN leverages prior experience, and learns how to optimize future queries more effectively (i.e., discovers better query plans) and efficiently (i.e., spending less time on optimization) compared with traditional optimizers. I will discuss potential challenges for future research and describe deep learning approaches that can lead the way to end-to-end learning-based query optimizers.

  • (1/23, 12:00pm): Panayiotis Tsaparas (University of Ioannina) details)

  • (1/28, 12:00pm): Stefano Ceri (Politecnico di Milano): WVH 366: Data-Driven Genomic Computing: Making Sense of the Signals from the Genome details)

    Data-Driven Genomic Computing: Making Sense of the Signals from the Genome

    Genomic computing is a new science focused on understanding the functioning of the genome, as a premise to fundamental discoveries in biology and medicine. Next Generation Sequencing (NGS) allows the production of the entire human genome sequence at a cost of about 1000 US $; many algorithms exist for the extraction of genome features, or "signals", including peaks (enriched regions), variants, or gene expression (intensity of transcription activity). The missing gap is a system supporting data integration and exploration, giving a “biological meaning” to all the available information; such a system can be used, e.g., for better understanding cancer or how environment influences cancer development.

    The GeCo Project (Data-Driven Genomic Computing, ERC Advanced Grant, 2016-2021) has the objective or revisiting genomic computing through the lens of basic data management, through models, languages, and instruments, focusing on genomic data integration. Starting from an abstract model, we developed a system that can be used to query processed data produced by several large Genomic Consortia, including Encode and TCGA; the system employs internally the Spark engine, and prototypes can already be accessed from Polimi, from Cineca (Italian supercomputing center) and from the Broad Institute in Cambridge. During the five-years of the ERC project, the system will be enriched with data analysis tools and environments and will be made increasingly efficient. Among the objectives of the project, the creation of an “open source” repository of public data, available to biological and clinical research through queries, web services and search interfaces.

    Stefano Ceri is professor of Database Systems at the Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB) of Politecnico di Milano. His research work covers four decades (1978-2018) and has been generally concerned with extending database technologies in order to incorporate new features: distribution, object-orientation, rules, streaming data; with the advent of the Web, his research has been targeted towards the engineering of Web-based applications and to search systems. More recently he turned to genomic computing. He authored over 350 publications (H-index 75) and authored or edited 15 books in English. He is the recipient of two ERC Advanced Grants: "Search Computing (SeCo)" (2008-2013), focused upon the rank-aware integration of search engines in order to support multi-domain queries and “Data-Centered Genomic Computing (GeCo)” (2016-2021), focused upon new abstractions for querying and integrating genomic datasets. He is the recipient of the ACM-SIGMOD "Edward T. Codd Innovation Award" (New York, June 26, 2013), an ACM Fellow and a member of Academia Europaea.


  • (1/28, 12:30pm): Marco Brambilla (Politecnico di Milano): details)

  • (2/1, 12:00pm): Huy Ngyuen (Northeastern) details)

  • (2/6, 12:00pm): Sarah Ostadabbas (Northeastern): Physics-based Simulation to Bootstrap Learning in Small Data Domains details)


Past Semesters

Spring 2018

Fall 2017

This semester the data lab seminar is combined with in our special topics class (CS 7290: Special topics: Foundations in scalable data management) and takes place every Tuesday 11:45am-1:25pm and Thursday 2:50-4:30pm in Ryder Hall 126 (map).


Related Seminars @NEU