My research spans a spectrum of topics in "big data", including database query optimization, adaptive query processing, cloud computing, sensor network data management, uncertain data management, and graph databases. Some of
the current and past research projects include:
- SourceSight: Enabling Effective Source Selection (Demonstration Proposal);
Theodoros Rekatsinas, Amol Deshpande, Xin Luna Dong, Lise Getoor, Divesh Srivastava;
SIGMOD 2016.
[abstract] Recently there has been a rapid increase in the number of data sources and data services, such as cloud-based data markets and data portals, that facilitate the collection, publishing and trading of data. Data sources typically exhibit large heterogeneity in the type and quality of data they provide. Unfortunately, when the number of data sources is large, it is difficult for users to reason about the actual usefulness of sources for their applications and the trade-offs between the benefits and costs of acquiring and integrating sources. In this demonstration we present SourceSight, a system that allows users to interactively explore a large number of heterogeneous data sources, and discover valuable sets of sources for diverse integration tasks. SourceSight uses a novel multi-level source quality index that enables effective source selection at different granularity levels, and introduces a collection of new techniques to discover and evaluate relevant sources for integration.
- Continuous Detection of Activity-based Subgraph Patterns on Dynamic Graphs;
Jayanta Mondal and Amol Deshpande;
DEBS 2016.
[abstract] The ability to detect and analyze interesting subgraph patterns on large and dynamic graph-structured data in real time is crucial for many applications. Such patterns often need to reason about how the nodes behave (i.e., the activity component) in the network as well as how they are connected to each other (i.e., the structural component). An example of such an activity-based subgraph pattern is a clique of users in a social network (the structural predicate), who each have posted more than 10 messages in last 2 hours (the activity-based predicate). Detecting such complex events and analyzing them in real time can help with anomaly detection in phone call networks, advertisement targeting in social networks, malware detection in file download graphs, and so on. In this paper, we present CASQD, a system for continuous detection and analysis of such active subgraph pattern queries over large dynamic graphs. Some of key challenges in executing such queries include: handling a wide variety of user-specified activities of interest, low selectivities of activity-based predicates and the resultant exponential search space, and high data rates often seen in these application domains. A key abstraction in CASQD is a notion called graph-view, which acts as an independence layer between the query language and the underlying physical representation of the graph and the active attributes. This abstraction is aimed at simplifying the query language, while empowering the query optimizer. Considering the balance between expressibility (i.e., patterns that cover many real-world use cases) and optimizability of such patterns, we primarily focus on efficient continuous detection of the active regular structures (specifically, active cliques, active stars, and active bi-cliques). We develop a series of optimization techniques including model-based neighborhood explorations, lazy evaluation of the activity predicates, neighborhood-based search space pruning, and others, for efficient query evaluation. We perform a thorough comparative study of the execution strategies under various settings, and show that our system is capable of achieving end-to-end throughputs over 800k/s using a single, powerful machine.
- NScaleSpark: Subgraph-centric Graph Analytics on Apache Spark;
Abdul Quamar and Amol Deshpande;
NDA 2016 (International Workshop on Network Data Analytics, co-located with SIGMOD 2016).
[abstract] In this paper, we describe NScaleSpark, a framework for executing large-scale distributed graph analysis tasks on the Apache Spark platform. NScaleSpark is motivated by the increasing interest in executing rich and complex analysis tasks over large graph datasets. There is much recent work on vertex-centric graph programming frameworks for executing such analysis tasks - these systems espouse a "think-like-a-vertex" (TLV) paradigm, with some example systems being Pregel, Apache Giraph, GPS, Grace, and GraphX (built on top of Apache Spark). However, the TLV paradigm is not suitable for many complex graph analysis tasks that typically require processing of information aggregated over neighborhoods or subgraphs in the underlying graph. Instead, NScaleSpark is based on a "think-like-a-subgraph" paradigm (also recently called "think-like-an-embedding"). Here, the users specify computations to be executed against a large number of multi-hop neighborhoods or subgraphs of the data graph. NScaleSpark builds upon our prior work on the NScale system, which was built on top of the Hadoop MapReduce system. This paper describes how we reimplemented NScale on the Apache Spark platform, and the key challenges therein, and the design decisions we made. NScaleSpark uses a series of RDD transformations to extract and hold the relevant subgraphs in distributed memory with minimal footprint using a cost-based optimizer. Our in-memory graph data structure enables efficient graph computations over large-scale graphs. Our experimental results over several real world data sets and applications show orders-of-magnitude improvement in performance and total cost over GraphX and other vertex-centric approaches.
- Decibel: The Relational Dataset Branching System;
Michael Maddox, David Goehring, Aaron Elmore, Samuel Madden, Aditya Parameswaran, Amol Deshpande;
VLDB 2016.
[abstract] As scientific endeavors and data analysis becomes increasingly collaborative, there is a need for data management systems that natively support the versioning or branching of datasets to enable concurrent analysis, cleaning, integration, manipulation, or curation of data across teams of individuals. Common practice for sharing and collaborating on datasets involves creating or storing multiple copies of the dataset, one for each stage of analysis, with no provenance information tracking the relationships between these datasets. This results not only in wasted storage, but also makes it challenging to track and integrate modifications made by different users to the same dataset. In this paper, we introduce the Relational Dataset Branching System, Decibel, a new relational storage system with built-in version control designed to address these shortcomings. We present our initial design for Decibel and provide a thorough evaluation of three versioned storage engine designs that focus on efficient query processing with minimal storage overhead. We also develop an exhaustive benchmark to enable the rigorous testing of these and future versioned storage engine designs.
- Storing and Analyzing Historical Graph Data at Scale;
Udayan Khurana, and Amol Deshpande;
EDBT 2016 (also CoRR Technical Report arXiv:1509.08960).
[abstract] The work on large-scale graph analytics to date has largely focused on the study of static properties of graph snapshots. However, a static view of interactions between entities is often an oversimplification of several complex phenomena like the spread of epidemics, information diffusion, formation of online communities}, and so on. Being able to find temporal interaction patterns, visualize the evolution of graph properties, or even simply compare them across time, adds significant value in reasoning over graphs. However, because of lack of underlying data management support, an analyst today has to manually navigate the added temporal complexity of dealing with large evolving graphs. In this paper, we present a system, called Historical Graph Store, that enables users to store large volumes of historical graph data and to express and run complex temporal graph analytical tasks against that data. It consists of two key components: a Temporal Graph Index (TGI), that compactly stores large volumes of historical graph evolution data in a partitioned and distributed fashion; it provides support for retrieving snapshots of the graph as of any timepoint in the past or evolution histories of individual nodes or neighborhoods; and a Spark-based Temporal Graph Analysis Framework (TAF), for expressing complex temporal analytical tasks and for executing them in an efficient and scalable manner. Our experiments demonstrate our system's efficient storage, retrieval and analytics across a wide variety of queries on large volumes of historical graph data.
- NScale: Neighborhood-centric Large-Scale Graph Analytics in the Cloud;
Abdul Quamar, Amol Deshpande, Jimmy Lin;
To appear in VLDB Journal (also CoRR Technical Report arXiv:1405.1499).
[pdf]
[abstract] There is an increasing interest in executing rich and complex analysis tasks over large-scale graphs, many of which require processing and reasoning about a large number of multi-hop neighborhoods or subgraphs in the graph. Examples of such tasks include ego network analysis, motif counting, finding social circles, personalized recommendations, analyzing influence cascades, etc. These tasks are not well served by the existing vertex-centric graph processing frameworks, whose computation, execution models limit the user program to directly access the state of a single vertex; this results in high communication, scheduling, and memory overheads in executing such tasks. Further, most existing graph processing frameworks typically ignore the challenges in extracting the relevant portion of the graph that an analysis task needs, and loading it onto distributed memory. In this paper, we describe NScale, a novel end-to-end graph processing framework that enables the distributed execution of complex subgraph-centric analytics over large-scale graphs in the cloud. NScale enables users to write programs at the level of subgraphs, and to specify the subgraphs of interest declaratively. NScale uses Apache YARN, a state-of-the-art framework for efficient and fault-tolerant distribution of data and computation. It features GEL, a novel graph extraction and loading phase, that extracts the relevant portions of the graph and utilizes a cost-based optimizer to partition and load the graph onto distributed memory using as few machines as possible to minimize the communication cost. It utilizes novel techniques for the distributed execution of user computation that minimize memory consumption by exploiting overlap among the subgraphs of interest. Our experimental results show orders-of-magnitude improvements in performance, and drastic reductions in the cost of analytics, over vertex-centric approaches.
- Towards a unified query language for provenance and versioning;
Amit Chavan, Silu Huang, Amol Deshpande, Aaron J. Elmore, Samuel Madden, Aditya Parameswaran;
USENIX TAPP 2015.
[pdf]
[abstract] Organizations and teams collect and acquire data from various sources, such as social interactions, financial transactions, sensor data, and genome sequencers. Different teams in an organization as well as different data scientists within a team are interested in extracting a variety of insights which require combining and collaboratively analyzing datasets in diverse ways. DataHub is a system that aims to provide robust version control and provenance management for such a scenario. To be truly useful for collaborative data science, one also needs the ability to specify queries and analysis tasks over the versioning and the provenance information in a unified manner. In this paper, we present an initial design of our query language, called VQuel, that aims to support such unified querying over both types of information, as well as the intermediate and final results of analyses. We also discuss some of the key language design and implementation challenges moving forward.
- GraphGen: Exploring Interesting Graphs in Relational Data (Demonstration Proposal);
Konstantinos Xirogiannopoulos, Udayan Khurana, and Amol Deshpande;
VLDB 2015.
[abstract] Analyzing interconnection structures among the data through the use of "graph algorithms" and "graph analytics" has been shown to provide tremendous value in many application domains. However, graphs are not the primary choice in the way that most data is currently stored, 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. This cumbersome and costly process not only raises barriers in using graph analytics, but also makes it hard to "explore" and "identify" hidden or implicit graphs in the data. Here we demonstrate a system, called "GraphGen", that enables users to declaratively specify graph extraction tasks over relational databases, to visually explore the extracted graphs, and to write and execute graph algorithms over them. We also demonstrate how unifying the extraction tasks and the graph algorithms enables significant optimizations that would not be possible otherwise.
- Collaborative Data Analytics with DataHub (Demonstration Proposal);
Anant Bhardwaj, Amol Deshpande, Aaron Elmore, David Karger, Sam Madden, Aditya Parameswaran, Harihar Subramanyam, Eugene Wu, Rebecca Zhang;
VLDB 2015.
[abstract] While there have been many solutions proposed for storing and analyzing large volumes of data, all of these solutions have limited support for collaborative data analytics, especially given the many individuals and teams are simultaneously analyzing, modifying and exchanging datasets, employing a number of heterogeneous tools or languages for data analysis, and writing scripts to clean, preprocess, or query data. We demonstrate DataHub, a unified platform with the ability to load, store, query, collaboratively analyze, interactively visualize, interface with external applications, and share datasets. We will demonstrate the following aspects of the DataHub platform: (a) flexible data storage, sharing, and native version- ing capabilities: multiple conference attendees can concurrently update the database and browse the different versions and inspect conflicts; (b) an app ecosystem that hosts apps for various data- processing activities: conference attendees will be able to effortlessly ingest, query, and visualize data using our existing apps; (c) thrift-based data serialization permits data analysis in any combination of 20+ languages, with DataHub as the common data store: conference attendees will be able to analyze datasets in R, Python, and Matlab, while the inputs and the results are still stored in DataHub. In particular, conference attendees will be able to use the DataHub notebook — an IPython-based notebook for analyzing data and storing the results of data analysis.
- Principles of Dataset Versioning: Exploring the Recreation/Storage Tradeoff;
Souvik Bhattacherjee, Amit Chavan, Silu Huang, Amol Deshpande, Aditya Parameswaran;
PVLDB 2015.
[pdf]
[summary blog post]
[abstract] The relative ease of collaborative data science and analysis has led to a proliferation of many thousands or millions of versions of the same datasets in many scientific and commercial domains, acquired or constructed at various stages of data analysis across many users, and often over long periods of time. Managing, storing, and recreating these dataset versions is a non-trivial task. The fundamental challenge here is the storage−recreation trade−off: the more storage we use, the faster it is to recreate or retrieve versions, while the less storage we use, the slower it is to recreate or retrieve versions. Despite the fundamental nature of this problem, there has been a surprisingly little amount of work on it. In this paper, we study this trade-off in a principled manner: we formulate six problems under various settings, trading off these quantities in various ways, demonstrate that most of the problems are intractable, and propose a suite of inexpensive heuristics drawing from techniques in delay-constrained scheduling, and spanning tree literature, to solve these problems. We have built a prototype version management system, that aims to serve as a foundation to our DATAHUB system for facilitating collaborative data science. We demonstrate, via extensive experiments, that our proposed heuristics provide efficient solutions in practical dataset versioning scenarios.
- CrowdGather: Entity Extraction over Structured Domains;
Theodoros Rekatsinas, Amol Deshpande, Aditya Parameswaran;
CoRR Technical Report arXiv:1502.06823, 2015.
[abstract] Crowdsourced entity extraction is often used to acquire data for many applications, including recommendation systems, construction of aggregated listings and directories, and knowledge base construction. Current solutions focus on entity extraction using a single query, e.g., only using "give me another restaurant", when assembling a list of all restaurants. Due to the cost of human labor, solutions that focus on a single query can be highly impractical. In this paper, we leverage the fact that entity extraction often focuses on {m structured domains}, i.e., domains that are described by a collection of attributes, each potentially exhibiting hierarchical structure. Given such a domain, we enable a richer space of queries, e.g., "give me another Moroccan restaurant in Manhattan that does takeout". Naturally, enabling a richer space of queries comes with a host of issues, especially since many queries return empty answers. We develop new statistical tools that enable us to reason about the gain of issuing {m additional queries} given little to no information, and show how we can exploit the overlaps across the results of queries for different points of the data domain to obtain accurate estimates of the gain. We cast the problem of {m budgeted entity extraction} over large domains as an adaptive optimization problem that seeks to maximize the number of extracted entities, while minimizing the overall extraction costs. We evaluate our techniques with experiments on both synthetic and real-world datasets, demonstrating a yield of up to 4X over competing approaches for the same budget.
- DataHub: Collaborative data science and dataset version management at scale;
Anant Bhardwaj, Souvik Bhattacherjee, Amit Chavan, Amol Deshpande, Aaron J. Elmore, Samuel Madden, Aditya Parameswaran;
CIDR 2015.
[pdf]
[abstract] Relational databases have limited support for data collaboration, where teams collaboratively curate and analyze large datasets. Inspired by software version control systems like git, we propose (a) a dataset version control system, giving users the ability to create, branch, merge, difference and search large, divergent collections of datasets, and (b) a platform, DataHub, that gives users the ability to perform collaborative data analysis building on this version control system. We outline the challenges in providing dataset version control at scale.
- VERTEXICA: Your Relational Friend for Graph Analytics!;
Alekh Jindal, Praynaa Rawlani, Eugene Wu, Samuel Madden, Amol Deshpande, Mike Stonebraker;
VLDB Demo 2014.
[pdf]
[abstract] In this paper, we present Vertexica, a graph analytics tools on top of a relational database, which is user friendly and yet highly efficient. Instead of constraining programmers to SQL, Vertexica offers a popular vertex-centric query interface, which is more natural for analysts to express many graph queries. The programmers simply provide their vertex-compute functions and Vertexica takes care of efficiently executing them in the standard SQL engine. The advantage of using Vertexica is its ability to leverage the relational features and enable much more sophisticated graph analysis. These include expressing graph algorithms which are difficult in vertex-centric but straightforward in SQL and the ability to compose end-to-end data processing pipelines, including pre- and post-processing of graphs as well as combining multiple algorithms for deeper insights. Vertexica has a graphical user interface and we outline several demonstration scenarios including, interactive graph analysis, complex graph analysis, and continuous and time series analysis.
- SWORD: Workload-aware Data Placement and Replica Selection for Cloud Data Management Systems;
Ashwin Kumar Kayyoor, Abdul Quamar, Amol Deshpande, Samir Khuller;
VLDB Journal (Special Issue on Data-Intensive Cloud Infrastructure), 23(6): 845-870, 2014.
[pdf]
[abstract] Cloud computing is increasingly being seen as a way to reduce infrastructure costs and add elasticity, and is being used by a wide range of organizations. Cloud data management systems today need to serve a range of different workloads, from analytical read-heavy workloads to transactional (OLTP) workloads. For both the service providers and the users, it is critical to minimize the consumption of resources like CPU, memory, communication bandwidth, and energy, without compromising on service-level agreements if any. In this article, we develop a workload-aware data placement and replication approach, called SWORD, for minimizing resource consumption in such an environment. Specifically, we monitor and model the expected workload as a hypergraph and develop partitioning techniques that minimize the average query span, i.e., the average number of machines involved in the execution of a query or a transaction. We empirically justify the use of query span as the metric to optimize, for both analytical and transactional workloads, and develop a series of replication and data placement algorithms by drawing connections to several well-studied graph theoretic concepts. We introduce a suite of novel techniques to achieve high scalability by reducing the overhead of partitioning and query routing. To deal with workload changes, we propose an incremental repartitioning technique that modifies data placement in small steps without resorting to complete repartitioning. We propose the use of fine-grained quorums defined at the level of groups of data items to control the cost of distributed updates, improve throughput, and adapt to different workloads. We empirically illustrate the benefits of our approach through a comprehensive experimental evaluation for two classes of workloads. For analytical read-only workloads, we show that our techniques result in significant reduction in total resource consumption. For OLTP workloads, we show that our approach improves transaction latencies and overall throughput by minimizing the number of distributed transactions.
- PStore: An Efficient Storage Framework for Managing Scientific Data;
Souvik Bhattacherjee, Amol Deshpande, and Alan Sussman;
SSDBM 2014.
[abstract] In this paper, we present the design, implementation, and evaluation of PStore, a no-overwrite storage framework for managing large volumes of array data generated by scientific simulations. PStore comprises of two modules, a data ingestion module and a query processing module, that respectively address two of the key challenges in scientific simulation data management. The data ingestion module is geared toward handling the high volumes of simulation data generated at a very rapid rate, which often makes it impossible to offload the data onto storage devices; the module is responsible for selecting an appropriate compression scheme for the data at hand, chunking the data, and then compressing it before sending it to the storage nodes. On the other hand, the query processing module is in charge of efficiently executing different types of queries over the stored data; in this paper, we specifically focus on slice (also called range) queries. PStore provides a suite of compression schemes that leverage existing techniques while extending some of them to provide support for diverse scientific simulation data. To efficiently execute queries over such compressed data, PStore adopts and extends a two-level chunking scheme by incorporating the effect of compression, and hides expensive disk latencies for long running range queries by exploiting chunk prefetching. In addition, we also parallelize the query processing module to further speed up execution. We evaluate PStore on a 140 GB dataset obtained from real-world simulations using the regional climate model CWRF. In this paper, we use both 3D and 4D datasets and demonstrate high performance through extensive experiments.
- EAGr: Supporting Continuous Ego-centric Aggregate Queries over Large Dynamic Graphs;
Jayanta Mondal and Amol Deshpande;
SIGMOD 2014 (also CoRR Technical Report arXiv:1404.6570).
[pdf]
[abstract] In this work, we present EAGr, a system for supporting large numbers of continuous neighborhood-based ("ego-centric") aggregate queries over large, highly dynamic, and rapidly evolving graphs. Examples of such queries include computation of personalized, tailored trends in social networks, anomaly/event detection in financial transaction networks, local search and alerts in spatio-temporal networks, to name a few. Key challenges in supporting such continuous queries include high update rates typically seen in these situations, large numbers of queries that need to be executed simultaneously, and stringent low latency requirements. We propose a flexible, general, and extensible in-memory framework for executing different types of ego-centric aggregate queries over large dynamic graphs with low latencies. Our framework is built around the notion of an aggregation overlay graph, a pre-compiled data structure that encodes the computations to be performed when an update/query is received. The overlay graph enables sharing of partial aggregates across multiple ego-centric queries (corresponding to the nodes in the graph), and also allows partial pre-computation of the aggregates to minimize the query latencies. We present several highly scalable techniques for constructing an overlay graph given an aggregation function, and also design incremental algorithms for handling structural changes to the underlying graph. We also present an optimal, polynomial-time algorithm for making the pre-computation decisions given an overlay graph, and evaluate an approach to incrementally adapt those decisions as the workload changes. Although our approach is naturally parallelizable, we focus on a single-machine deployment and show that our techniques can easily handle graphs of size up to 320 million nodes and edges, and achieve update/query throughputs of over 500K/s using a single, powerful machine.
- Optimization Techniques for "Scaling Down" Hadoop on Multi-Core, Shared-Memory Systems;
K. Ashwin Kumar, Jonathan Gluck, Amol Deshpande, Jimmy Lin;
EDBT 2014.
[pdf]
[abstract] The underlying assumption behind Hadoop and, more generally, the need for distributed processing is that the data to be analyzed cannot be held in memory on a single machine. Today, this assumption needs to be re-evaluated. Although petabyte-scale datastores are increasingly common, it is unclear whether ``typical'' analytics tasks require more than a single high-end server. Additionally, we are seeing increased sophistication in analytics, e.g., machine learning, which generally operate over smaller and more refined datasets. To address these trends, we propose ``scaling down'' Hadoop to run on shared-memory machines. This paper presents a prototype runtime called Hone, intended to be both API and binary compatible with standard (distributed) Hadoop. That is, Hone can take an existing Hadoop jar and run it, without modification, on a multi-core shared-memory machine. This allows us to take existing Hadoop algorithms and find the most suitable runtime environment for execution on datasets of varying sizes. Our experiments show that Hone order of magnitude faster than Hadoop pseudo-distributed mode (PDM); on dataset sizes that fit into memory, Hone outperforms a fully-distributed 15-node Hadoop cluster in some cases as well.
- Subgraph Pattern Matching over Uncertain Graphs with Identity Linkage Uncertainty;
Walaa Eldin Moustafa, Angelika Kimmig, Amol Deshpande, Lise Getoor;
ICDE 2014 (also CoRR Technical Report arXiv:1305.7006).
[pdf]
[abstract] There is a growing need for methods which can capture uncertainties and answer queries over graph-structured data. Two common types of uncertainty are uncertainty over the attribute values of nodes and uncertainty over the existence of edges. In this paper, we combine those with identity uncertainty. Identity uncertainty represents uncertainty over the mapping from objects mentioned in the data, or references, to the underlying real-world entities. We propose the notion of a probabilistic entity graph (PEG), a probabilistic graph model that defines a distribution over possible graphs at the entity level. The model takes into account node attribute uncertainty, edge existence uncertainty, and identity uncertainty, and thus enables us to systematically reason about all three types of uncertainties in a uniform manner. We introduce a general framework for constructing a PEG given uncertain data at the reference level and develop highly efficient algorithms to answer subgraph pattern matching queries in this setting. Our algorithms are based on two novel ideas: context-aware path indexing and reduction by join-candidates, which drastically reduce the query search space. A comprehensive experimental evaluation shows that our approach outperforms baseline implementations by orders of magnitude.
- Stream Querying and Reasoning on Social Data;
Jayanta Mondal and Amol Deshpande;
Chapter in Encyclopedia of Social Network Analysis and Mining, Springer, 2014.
[pdf]
[abstract] In this paper, we present an introduction to the new research area of "stream querying and reasoning" over social data. This area combines aspects from several well-studied research areas, chief among them, social network analysis, graph databases, and data streams. We provide a formal definition of the problem, survey the related prior work, and discuss some of the key research challenges that need to be addressed (and some of the solutions that have been proposed). We note that we use the term "stream reasoning" in this paper to encompass a broad range of tasks including various types of analytics, probabilistic reasoning, statistical inference, and logical reasoning. We contrast our use of this term with the recent work where this term has been used more specifically to refer to integration of logical reasoning systems with data streams in the context of the Semantic Web. Given the vast amount of work on this and related topics, it is not our intention to be comprehensive in this brief overview. Rather we aim to cover some of the key ideas and representative work.
- Approximation Algorithms for Stochastic Boolean Function Evaluation and Stochastic Submodular Set Cover;
Amol Deshpande, Lisa Hellerstein, and Devorah Kletenik;
SODA 2014 (also CoRR Technical Report arXiv:1303.0726).
[pdf]
[abstract] Stochastic Boolean Function Evaluation is the problem of determining the value of a given Boolean function f on an unknown input x, when each bit of x_i of x can only be determined by paying an associated cost c_i. The assumption is that x is drawn from a given product distribution, and the goal is to minimize the expected cost. This problem has been studied in Operations Research, where it is known as "sequential testing" of Boolean functions. It has also been studied in learning theory in the context of learning with attribute costs. We consider the general problem of developing approximation algorithms for Stochastic Boolean Function Evaluation. We give a 3-approximation algorithm for evaluating Boolean linear threshold formulas. We also present an approximation algorithm for evaluating CDNF formulas (and decision trees) achieving a factor of O(log kd), where k is the number of terms in the DNF formula, and d is the number of clauses in the CNF formula. In addition, we present approximation algorithms for simultaneous evaluation of linear threshold functions, and for ranking of linear functions. Our function evaluation algorithms are based on reductions to the Stochastic Submodular Set Cover (SSSC) problem. This problem was introduced by Golovin and Krause. They presented an approximation algorithm for the problem, called Adaptive Greedy. Our main technical contribution is a new approximation algorithm for the SSSC problem, which we call Adaptive Dual Greedy. It is an extension of the Dual Greedy algorithm for Submodular Set Cover due to Fujito, which is a generalization of Hochbaum's algorithm for the classical Set Cover Problem. We also give a new bound on the approximation achieved by the Adaptive Greedy algorithm of Golovin and Krause.
- SPARSI: Partitioning Sensitive Data amongst Multiple Adversaries;
Theodoros Rekatsinas, Amol Deshpande, and Ashwin Machanavajjhala;
PVLDB 2013, to be presented at VLDB 2014 (also CoRR Technical Report arXiv:1302.6556).
[pdf]
[abstract] We present SPARSI, a theoretical framework for partitioning sensitive data across multiple non-colluding adversaries. Most work in privacy-aware data sharing has considered disclosing summaries where the aggregate information about the data is preserved, but sensitive user information is protected. Nonetheless, there are applications, including online advertising, cloud computing and crowdsourcing markets, where detailed and fine-grained user-data must be disclosed. We consider a new data sharing paradigm and introduce the problem of privacy-aware data partitioning, where a sensitive dataset must be partitioned among k untrusted parties (adversaries). The goal is to maximize the utility derived by partitioning and distributing the dataset, while minimizing the amount of sensitive information disclosed. The data should be distributed so that an adversary, without colluding with other adversaries, cannot draw additional inferences about the private information, by linking together multiple pieces of information released to her. The assumption of no collusion is both reasonable and necessary in the above application domains that require release of private user information. SPARSI enables us to formally define privacy-aware data partitioning using the notion of sensitive properties for modeling private information and a hypergraph representation for describing the interdependencies between data entries and private information. We show that solving privacy-aware partitioning is, in general, NP-hard, but for specific information disclosure functions, good approximate solutions can be found using relaxation techniques. Finally, we present a local search algorithm applicable to generic information disclosure functions. We apply SPARSI together with the proposed algorithms on data from a real advertising scenario and show that we can partition data with no disclosure to any single advertiser.
- Efficient Snapshot Retrieval over Historical Graph Data;
Udayan Khurana, and Amol Deshpande;
ICDE 2013 (also CoRR Technical Report arXiv:1207.5777).
[pdf]
[abstract] We address the problem of managing historical data for large evolving information networks like social networks or citation networks, with the goal to enable temporal and evolutionary queries and analysis. We present the design and architecture of a distributed graph database system that stores the entire history of a network and provides support for efficient retrieval of multiple graphs from arbitrary time points in the past, in addition to maintaining the current state for ongoing updates. Our system exposes a general programmatic API to process and analyze the retrieved snapshots. We introduce DeltaGraph, a novel, extensible, highly tunable, and distributed hierarchical index structure that enables compactly recording the historical information, and that supports efficient retrieval of historical graph snapshots for single-site or parallel processing. Along with the original graph data, DeltaGraph can also maintain and index auxiliary information; this functionality can be used to extend the structure to efficiently execute queries like subgraph pattern matching over historical data. We develop analytical models for both the storage space needed and the snapshot retrieval times to aid in choosing the right parameters for a specific scenario. In addition, we present strategies for materializing portions of the historical graph state in memory to further speed up the retrieval process. Secondly, we present an in-memory graph data structure called GraphPool that can maintain hundreds of historical graph instances in main memory in a non-redundant manner. We present a comprehensive experimental evaluation that illustrates the effectiveness of our proposed techniques at managing historical graph information.
- Algorithms for the Thermal Scheduling Problem;
Koyel Mukherjee, Samir Khuller, and Amol Deshpande;
IPDPS 2013.
[pdf]
[abstract] The energy costs for cooling a data center constitute a significant portion of the overall running costs. Thermal imbalance and hot spots that arise due to imbalanced workloads lead to significant wasted cooling effort -- in order to ensure that no equipment is operating above a certain temperature, the data center may be cooled more than necessary. Therefore it is desirable to schedule the workload in a data center in a "thermally aware" manner, assigning jobs to machines not just based on local load of the machines, but based on the overall thermal profile of the data center. This is challenging because of the spatial cross-interference between machines, where a job assigned to a machine may impact not only that machine's temperature, but also nearby machines. Here, we continue formal analysis of the "thermal scheduling" problem that we initiated recently. There we introduced the notion of "effective load of a machine" which is a function of the local load on the machine as well as the load on nearby machines, and presented optimal scheduling policies for a simple model (where cross-effects are restricted within a rack) under the assumption that jobs can be split among different machines. Here we consider the more realistic problem of "integral" assignment of jobs, and allow for cross-interference among different machines in adjacent racks in the data center. The integral assignment problem with cross-interference is NP-hard, even for a simple two machine model. We consider three different heat flow models, and give constant factor approximation algorithms for maximizing the number (or total profit) of jobs assigned in each model, without violating thermal constraints. We also consider the problem of minimizing the maximum temperature on any machine when all jobs need to be assigned, and give constant factor algorithms for this problem.
- Efficiently Adapting Graphical Models for Selectivity Estimation;
Kostas Tzoumas, Amol Deshpande, and Christian Jensen;
VLDB Journal (Special Issue: Best Papers of VLDB 2011), 22(1): 3-27, February 2013.
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[abstract] Query optimizers rely on statistical models that succinctly describe the underlying data. Models are used to derive cardinality estimates for intermediate relations, which in turn guide the optimizer to choose the best query execution plan. The quality of the resulting plan is highly dependent on the accuracy of the statistical model that represents the data. It is well known that small errors in the model estimates propagate exponentially through joins, and may result in the choice of a highly sub-optimal query execution plan. Most commercial query optimizers make the attribute value independence assumption: all attributes are assumed to be statistically independent. This reduces the statistical model of the data to a collection of one-dimensional synopses (typically in the form of histograms), and it permits the optimizer to estimate the selectivity of a predicate conjunction as the product of the selectivities of the constituent predicates. However, this independence assumption is more often than not wrong, and is considered to be the most common cause of sub-optimal query execution plans chosen by modern query optimizers. We take a step towards a principled and practical approach to performing cardinality estimation without making the independence assumption. By carefully using concepts from the field of graphical models, we are able to factor the joint probability distribution over all the attributes in the database into small, usually two-dimensional distributions, without a significant loss in estimation accuracy. We show how to efficiently construct such a graphical model from the database using only two-way join queries, and we show how to perform selectivity estimation in a highly efficient manner. We integrate our algorithms into the PostgreSQL DBMS. Experimental results indicate that estimation errors can be greatly reduced, leading to orders of magnitude more efficient query execution plans in many cases. Optimization time is kept in the range of tens of milliseconds, making this a practical approach for industrial-strength query optimizers.
- Managing Large Dynamic Graphs Efficiently;
Jayanta Mondal, Amol Deshpande;
SIGMOD 2012.
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[abstract] There is an increasing need to ingest, manage, and query large volumes of graph-structured data arising in applications like social networks, communication networks, biological networks etc. Graph databases that can explicitly reason about the graphical nature of the data, that can support flexible schemas and node/edge-centric analysis and querying, are ideal for storing such data. However, although there is much work on single-site graph databases and on efficiently executing specific types of queries over large graphs, to date there is little work on understanding the challenges in distributed dynamic graph data management, needed to handle the large scale of such data. In this paper, we propose the design of an in-memory, distributed graph data management system aimed at managing a large dynamically changing graph and supporting low-latency query processing over it. The key challenge in a distributed graph database is that, partitioning a graph across a set of machines inherently results in a large number of distributed traversals across partitions to answer even simple queries. We propose a suite of novel techniques to minimize the communication bandwidth and the storage requirements. We have implemented our framework as a middle-ware on top of an open-source key-value store. We evaluate our system on a social graph, and show that our system is able to handle very large graphs efficiently, and that it reduces the network bandwidth consumption significantly.
- Maximizing Expected Utility for Stochastic Combinatorial Optimization Problems;
Jian Li, and Amol Deshpande;
FOCS 2011 (also CoRR Technical Report arXiv:1012.3189).
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[abstract] We study the stochastic versions of a broad class of combinatorial problems where the weights of the elements in the input dataset are uncertain. The class of problems that we study includes shortest paths, minimum weight spanning trees, and minimum weight matchings over probabilistic graphs, and other combinatorial problems like knapsack. We observe that the expected value is inadequate in capturing different types of "risk-averse" or "risk-prone" behaviors, and instead we consider a more general objective which is to maximize the "expected utility" of the solution for some given utility function, rather than the expected weight (expected weight becomes a special case). We show that we can obtain a polynomial time approximation algorithm with "additive error" "epsilon" for any "epsilon>0", if there is a pseudopolynomial time algorithm for the "exact" version of the problem. (This is true for the problems mentioned above). Our result generalizes several prior results on stochastic shortest path, stochastic spanning tree, and stochastic knapsack. Our algorithm for utility maximization makes use of the separability of exponential utility and a technique to decompose a general utility function into exponential utility functions, which may be useful in other stochastic optimization problems.
- Sensitivity Analysis and Explanations for Robust Query Evaluation in Probabilistic Databases;
Bhargav Kanagal, Jian Li, Amol Deshpande;
SIGMOD 2011.
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[abstract] Probabilistic database systems have successfully established themselves as a tool for managing uncertain data. However, much of the research in this area has focused on efficient query evaluation and has largely ignored two key issues that commonly arise in uncertain data management: First, how to provide "explanations" for query results, e.g., ``Why is this tuple in my result?'' or ``Why does this output tuple have such high probability?''. Second, the problem of determining the "sensitive" input tuples for the given query, e.g., users are interested to know the input tuples that can substantially alter the output, when their probabilities are modified (since they may be unsure about the input probability values). Existing systems provide the "lineage/provenance" of each of the output tuples in addition to the output probabilities, which is a boolean formula indicating the dependence of the output tuple on the input tuples. However, lineage does not immediately provide a quantitative relationship and it is not informative when we have multiple output tuples. In this paper, we propose a unified framework that can handle both the issues mentioned above to facilitate robust query processing. We formally define the notions of "influence" and "explanations" and provide algorithms to determine the top-l influential set of variables and the top-l set of explanations for a variety of queries, including "conjunctive" queries, "probabilistic threshold" queries, "top-k" queries and "aggregation" queries. Further, our framework naturally enables highly efficient incremental evaluation when input probabilities are modified (e.g., if uncertainty is resolved). Our preliminary experimental results demonstrate the benefits of our framework for performing robust query processing over probabilistic databases.
- PrDB: Managing and Exploiting Rich Correlations in Probabilistic Databases;
Prithviraj Sen, Amol Deshpande, and Lise Getoor;
VLDB Journal Special Issue on Uncertain and Probabilistic Databases, 18(6): 1065-1090, 2009.
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[abstract] Due to numerous applications producing noisy data, e.g., sensor data, experimental data, data from uncurated sources, information extraction, etc., there has been a surge of interest in the development of probabilistic databases. Most probabilistic database models proposed to date, however, fail to meet the challenges of real-world applications on two counts: (1) they often restrict the kinds of uncertainty that the user can represent; and (2) the query processing algorithms often cannot scale up to the needs of the application. In this work, we define a probabilistic database model, "PrDB", that uses graphical models, a state-of-the-art probabilistic modeling technique developed within the statistics and machine learning community, to model uncertain data. We show how this results in a rich, complex yet compact probabilistic database model, which can capture the commonly occurring uncertainty models (tuple uncertainty, attribute uncertainty), more complex models (correlated tuples and attributes) and allows compact representation (shared and schema-level correlations). In addition, we show how query evaluation in PrDB translates into inference in an appropriately augmented graphical model. This allows us to easily use any of a myriad of exact and approximate inference algorithms developed within the graphical modeling community. While probabilistic inference provides a generic approach to solving queries, we show how the use of shared correlations, together with a novel inference algorithm that we developed based on bisimulation, can speed query processing significantly. We present a comprehensive experimental evaluation of the proposed techniques and show that even with a few shared correlations, significant speedups are possible.
- A Unified Approach to Ranking in Probabilistic Databases;
Jian Li and Barna Saha and Amol Deshpande;
VLDB 2009 (also CoRR Technical Report arXiv:0904.1366).
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[abstract] The dramatic growth in the number of application domains that naturally generate probabilistic, uncertain data has resulted in a need for efficiently supporting complex querying and decision-making over such data. In this paper, we present a unified approach to ranking and top-k query processing in probabilistic databases by viewing it as a multi-criteria optimization problem, and by deriving a set of features that capture the key properties of a probabilistic dataset that dictate the ranked result. We contend that a single, specific ranking function may not suffice for probabilistic databases, and we instead propose two parameterized ranking functions, called PRF-w and PRF-e, that generalize or can approximate many of the previously proposed ranking functions. We present novel generating functions-based algorithms for efficiently ranking large datasets according to these ranking functions, even if the datasets exhibit complex correlations modeled using probabilistic and/xor trees or Markov networks. We further propose that the parameters of the ranking function be learned from user preferences, and we develop an approach to learn those parameters. Finally, we present a comprehensive experimental study that illustrates the effectiveness of our parameterized ranking functions, especially PRF-e, at approximating other ranking functions and the scalability of our proposed algorithms for exact or approximate ranking.
- Online Filtering, Smoothing and Probabilistic Modeling of Streaming data;
Bhargav Kanagal, Amol Deshpande;
ICDE 2008.
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[abstract] In this paper, we address the problem of extending a relational database system to facilitate efficient real-time application of dynamic probabilistic models to streaming data. We use the recently proposed abstraction of model-based views for this purpose, by allowing users to declaratively specify the model to be applied, and by presenting the output of the models to the user as a probabilistic database view. We support declarative querying over such views using an extended version of SQL that allows for querying probabilistic data. Underneath we use particle filters, a class of sequential Monte Carlo algorithms, to represent the present and historical states of the model as sets of weighted samples (particles) that are kept up-to-date as new data arrives. We develop novel techniques to convert the queries on the model-based view directly into queries over particle tables, enabling highly efficient query processing. Finally, we present experimental evaluation of our prototype implementation over several synthetic and real datasets, that demonstrates the feasibility of online modeling of streaming data using our system and establishes the advantages of tight integration between dynamic probabilistic models and databases.
- Adaptive Query Processing;
Amol Deshpande, Zack Ives, Vijayshankar Raman;
Foundations and Trends in Databases, 1(1), 2007.
[abstract] As the data management field has diversified to consider settings in which queries are increasingly complex, statistics are less available, or data is stored remotely, there has been an acknowledgment that the traditional optimize-then-execute paradigm is insufficient. This has led to a plethora of new techniques, generally placed under the common banner of adaptive query processing, that focus on using runtime feedback to modify query processing in a way that provides better response time or more efficient CPU utilization.
In this survey paper, we identify many of the common issues, themes, and approaches that pervade this work, and the settings in which each piece of work is most appropriate. Our goal with this paper is to be a "value-add" over the existing papers on the material, providing not only a brief overview of each technique, but also a basic framework for understanding the field of adaptive query processing in general. We focus primarily on intra-query adaptivity of long-running, but not full-fledged streaming, queries. We conclude with a discussion of open research problems that are of high importance.
- MauveDB: Supporting Model-based User Views in Database Systems;
Amol Deshpande, Sam Madden;
SIGMOD 2006.
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[abstract] Real-world data --- especially when generated by distributed measurement infrastructures such as sensor networks --- tends to be incomplete, imprecise, and erroneous, making it impossible to present it to users or feed it directly into applications. The traditional approach to dealing with this problem is to first process the data using statistical or probabilistic "models" that can provide more robust interpretations of the data. Current database systems, however, do not provide adequate support for applying models to such data, especially when those models need to be frequently updated as new data arrives in the system. Hence, most scientists and engineers, who depend on models for managing their data, do not use database systems for archival or querying at all; at best, databases serve as a persistent raw data store.
In this paper we define a new abstraction called "model-based views" and present the architecture of "MauveDB", the system we are building to support such views. Just as traditional database views provide logical data independence, model-based views provide independence from the details of the underlying data generating mechanism and hide the irregularities of the data by using models to present a consistent view to the users. MauveDB supports a declarative language for defining model-based views, allows declarative querying over such views using SQL, and supports several different materialization strategies and techniques to efficiently maintain them in the face of frequent updates. We have implemented a prototype system that currently supports views based on regression and interpolation, in the Apache Derby open source DBMS, and we present results that show the utility and performance benefits that can be obtained by supporting several different types of model-based views in a database system.
- Approximate Data Collection in Sensor Networks using Probabilistic Models;
David Chu, Amol Deshpande, Joseph M. Hellerstein, Wei Hong;
ICDE 2006.
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[abstract] Wireless sensor networks are proving to be useful in a variety of settings. A core challenge in these networks is to minimize energy consumption. Prior database research has proposed to achieve this by pushing data-reducing operators like aggregation and selection down into the network. This approach has proven unpopular with early adopters of sensor network technology, who typically want to extract complete "dumps" of the sensor readings, i.e., to run "SELECT *" queries. Unfortunately, because these queries do no data reduction, they consume significant energy in current sensornet query processors.
In this paper we attack the "SELECT *" problem for sensor networks. We propose a robust approximate technique called "Ken" that uses "replicated dynamic probabilistic models" to minimize communication from sensor nodes to the network's PC base station. In addition to data collection, we show that Ken is well suited to anomaly- and event-detection applications.
A key challenge in this work is to intelligently exploit spatial correlations "across" sensor nodes without imposing undue sensor-to-sensor communication burdens to maintain the models. Using traces from two real-world sensor network deployments, we demonstrate that relatively simple models can provide significant communication (and hence energy) savings without undue sacrifice in result quality or frequency. Choosing optimally among even our simple models is NP-hard, but our experiments show that a greedy heuristic performs nearly as well as an exhaustive algorithm.
- Model-Driven Data Acquisition in Sensor Networks;
Amol Deshpande, Carlos Guestrin, Sam Madden, Joseph M. Hellerstein, Wei Hong;
VLDB 2004.
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[abstract] Declarative queries are proving to be an attractive paradigm for interacting with networks of wireless sensors. The metaphor that "the sensornet is a database" is problematic, however, because sensors do not exhaustively represent the data in the real world. In order to map the raw sensor readings onto physical reality, a "model" of that reality is required to complement the readings. In this paper, we enrich interactive sensor querying with statistical modeling techniques. We demonstrate that such models can help provide answers that are both more meaningful, and, by introducing approximations with probabilistic confidences, significantly more efficient to compute in both time and energy. Utilizing the combination of a model and live data acquisition raises the challenging optimization problem of selecting the best sensor readings to acquire, balancing the increase in the confidence of our answer against the communication and data acquisition costs in the network. We describe an exponential time algorithm for finding the optimal solution to this optimization problem, and a polynomial-time heuristic for identifying solutions that perform well in practice. We evaluate our approach on several real-world sensor-network data sets, taking into account the real measured data and communication quality, demonstrating that our model-based approach provides a high-fidelity representation of the real phenomena and leads to significant performance gains versus traditional data acquisition techniques.