2,889 research outputs found
Speculative Approximations for Terascale Analytics
Model calibration is a major challenge faced by the plethora of statistical
analytics packages that are increasingly used in Big Data applications.
Identifying the optimal model parameters is a time-consuming process that has
to be executed from scratch for every dataset/model combination even by
experienced data scientists. We argue that the incapacity to evaluate multiple
parameter configurations simultaneously and the lack of support to quickly
identify sub-optimal configurations are the principal causes. In this paper, we
develop two database-inspired techniques for efficient model calibration.
Speculative parameter testing applies advanced parallel multi-query processing
methods to evaluate several configurations concurrently. The number of
configurations is determined adaptively at runtime, while the configurations
themselves are extracted from a distribution that is continuously learned
following a Bayesian process. Online aggregation is applied to identify
sub-optimal configurations early in the processing by incrementally sampling
the training dataset and estimating the objective function corresponding to
each configuration. We design concurrent online aggregation estimators and
define halting conditions to accurately and timely stop the execution. We apply
the proposed techniques to distributed gradient descent optimization -- batch
and incremental -- for support vector machines and logistic regression models.
We implement the resulting solutions in GLADE PF-OLA -- a state-of-the-art Big
Data analytics system -- and evaluate their performance over terascale-size
synthetic and real datasets. The results confirm that as many as 32
configurations can be evaluated concurrently almost as fast as one, while
sub-optimal configurations are detected accurately in as little as a
fraction of the time
Distributed top-k aggregation queries at large
Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network
Efficient Subgraph Matching on Billion Node Graphs
The ability to handle large scale graph data is crucial to an increasing
number of applications. Much work has been dedicated to supporting basic graph
operations such as subgraph matching, reachability, regular expression
matching, etc. In many cases, graph indices are employed to speed up query
processing. Typically, most indices require either super-linear indexing time
or super-linear indexing space. Unfortunately, for very large graphs,
super-linear approaches are almost always infeasible. In this paper, we study
the problem of subgraph matching on billion-node graphs. We present a novel
algorithm that supports efficient subgraph matching for graphs deployed on a
distributed memory store. Instead of relying on super-linear indices, we use
efficient graph exploration and massive parallel computing for query
processing. Our experimental results demonstrate the feasibility of performing
subgraph matching on web-scale graph data.Comment: VLDB201
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Complex Query Operators on Modern Parallel Architectures
Identifying interesting objects from a large data collection is a fundamental problem for multi-criteria decision making applications.In Relational Database Management Systems (RDBMS), the most popular complex query operators used to solve this type of problem are the Top-K selection operator and the Skyline operator.Top-K selection is tasked with retrieving the k-highest ranking tuples from a given relation, as determined by a user-defined aggregation function.Skyline selection retrieves those tuples with attributes offering (pareto) optimal trade-offs in a given relation.Efficient Top-K query processing entails minimizing tuple evaluations by utilizing elaborate processing schemes combined with sophisticated data structures that enable early termination.Skyline query evaluation involves supporting processing strategies which are geared towards early termination and incomparable tuple pruning.The rapid increase in memory capacity and decreasing costs have been the main drivers behind the development of main-memory database systems.Although the act of migrating query processing in-memory has created many opportunities to improve the associated query latency, attaining such improvements has been very challenging due to the growing gap between processor and main memory speeds.Addressing this limitation has been made easier by the rapid proliferation of multi-core and many-core architectures.However, their utilization in real systems has been hindered by the lack of suitable parallel algorithms that focus on algorithmic efficiency.In this thesis, we study in depth the Top-K and Skyline selection operators, in the context of emerging parallel architectures.Our ultimate goal is to provide practical guidelines for developing work-efficient algorithms suitable for parallel main memory processing.We concentrate on multi-core (CPU), many-core (GPU), and processing-in-memory architectures (PIM), developing solutions optimized for high throughout and low latency.The first part of this thesis focuses on Top-K selection, presenting the specific details of early termination algorithms that we developed specifically for parallel architectures and various types of accelerators (i.e. GPU, PIM).The second part of this thesis, concentrates on Skyline selection and the development of a massively parallel load balanced algorithm for PIM architectures.Our work consolidates performance results across different parallel architectures using synthetic and real data on variable query parameters and distributions for both of the aforementioned problems.The experimental results demonstrate several orders of magnitude better throughput and query latency, thus validating the effectiveness of our proposed solutions for the Top-K and Skyline selection operators
Capturing Topology in Graph Pattern Matching
Graph pattern matching is often defined in terms of subgraph isomorphism, an
NP-complete problem. To lower its complexity, various extensions of graph
simulation have been considered instead. These extensions allow pattern
matching to be conducted in cubic-time. However, they fall short of capturing
the topology of data graphs, i.e., graphs may have a structure drastically
different from pattern graphs they match, and the matches found are often too
large to understand and analyze. To rectify these problems, this paper proposes
a notion of strong simulation, a revision of graph simulation, for graph
pattern matching. (1) We identify a set of criteria for preserving the topology
of graphs matched. We show that strong simulation preserves the topology of
data graphs and finds a bounded number of matches. (2) We show that strong
simulation retains the same complexity as earlier extensions of simulation, by
providing a cubic-time algorithm for computing strong simulation. (3) We
present the locality property of strong simulation, which allows us to
effectively conduct pattern matching on distributed graphs. (4) We
experimentally verify the effectiveness and efficiency of these algorithms,
using real-life data and synthetic data.Comment: VLDB201
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