73 research outputs found
Scalable Audience Reach Estimation in Real-time Online Advertising
Online advertising has been introduced as one of the most efficient methods
of advertising throughout the recent years. Yet, advertisers are concerned
about the efficiency of their online advertising campaigns and consequently,
would like to restrict their ad impressions to certain websites and/or certain
groups of audience. These restrictions, known as targeting criteria, limit the
reachability for better performance. This trade-off between reachability and
performance illustrates a need for a forecasting system that can quickly
predict/estimate (with good accuracy) this trade-off. Designing such a system
is challenging due to (a) the huge amount of data to process, and, (b) the need
for fast and accurate estimates. In this paper, we propose a distributed fault
tolerant system that can generate such estimates fast with good accuracy. The
main idea is to keep a small representative sample in memory across multiple
machines and formulate the forecasting problem as queries against the sample.
The key challenge is to find the best strata across the past data, perform
multivariate stratified sampling while ensuring fuzzy fall-back to cover the
small minorities. Our results show a significant improvement over the uniform
and simple stratified sampling strategies which are currently widely used in
the industry
A Survey on Array Storage, Query Languages, and Systems
Since scientific investigation is one of the most important providers of
massive amounts of ordered data, there is a renewed interest in array data
processing in the context of Big Data. To the best of our knowledge, a unified
resource that summarizes and analyzes array processing research over its long
existence is currently missing. In this survey, we provide a guide for past,
present, and future research in array processing. The survey is organized along
three main topics. Array storage discusses all the aspects related to array
partitioning into chunks. The identification of a reduced set of array
operators to form the foundation for an array query language is analyzed across
multiple such proposals. Lastly, we survey real systems for array processing.
The result is a thorough survey on array data storage and processing that
should be consulted by anyone interested in this research topic, independent of
experience level. The survey is not complete though. We greatly appreciate
pointers towards any work we might have forgotten to mention.Comment: 44 page
system architecture for approximate query processing
Decision making is an activity that addresses the problem of extracting knowledge and information from data stored in data warehouses, in order to improve the business processes of information systems. Usually, decision making is based on On-Line Analytical Processing, data mining, or approximate query processing. In the last case, answers to analytical queries are provided in a fast manner, although affected with a small percentage of error. In the paper, we present the architecture of an approximate query answering system. Then, we illustrate our ADAP (Analytical Data Profile) system, which is based on an engine able to provide fast responses to the main statistical functions by using orthogonal polynomials series to approximate the data distribution of multidimensional relations. Moreover, several experimental results to measure the approximation error are shown and the response-time to analytical queries is reported.</p
Time Series Management Systems:A Survey
The collection of time series data increases as more monitoring and
automation are being deployed. These deployments range in scale from an
Internet of things (IoT) device located in a household to enormous distributed
Cyber-Physical Systems (CPSs) producing large volumes of data at high velocity.
To store and analyze these vast amounts of data, specialized Time Series
Management Systems (TSMSs) have been developed to overcome the limitations of
general purpose Database Management Systems (DBMSs) for times series
management. In this paper, we present a thorough analysis and classification of
TSMSs developed through academic or industrial research and documented through
publications. Our classification is organized into categories based on the
architectures observed during our analysis. In addition, we provide an overview
of each system with a focus on the motivational use case that drove the
development of the system, the functionality for storage and querying of time
series a system implements, the components the system is composed of, and the
capabilities of each system with regard to Stream Processing and Approximate
Query Processing (AQP). Last, we provide a summary of research directions
proposed by other researchers in the field and present our vision for a next
generation TSMS.Comment: 20 Pages, 15 Figures, 2 Tables, Accepted for publication in IEEE TKD
IDEAS-1997-2021-Final-Programs
This document records the final program for each of the 26 meetings of the International Database and Engineering Application Symposium from 1997 through 2021. These meetings were organized in various locations on three continents. Most of the papers published during these years are in the digital libraries of IEEE(1997-2007) or ACM(2008-2021)
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