165 research outputs found
On the complexity of privacy-preserving complex event processing
Complex Event Processing (CEP) Systems are stream processing systems that monitor incoming event streams in search of user-specified event patterns. While CEP systems have been adopted in a variety of applications, the privacy implications of event pattern reporting mechanisms have yet to be studied — a stark contrast to the significant amount of attention that has been devoted to privacy for relational systems. In this paper we present a privacy problem that arises when the system must support desired patterns (those that should be reported if detected) and private patterns (those that should not be revealed). We formalize this problem, which we term privacy-preserving, utility maximizing CEP (PP-CEP), and analyze its complexity under various assumptions. Our results show that this is a rich problem to study and shed some light on the difficulty of developing algorithms that preserve utility without compromis-ing privacy
Holistic Cube Analysis: A Query Framework for Data Insights
We present Holistic Cube Analysis (HoCA), a framework that augments the
capabilities of relational queries for data insights. We first define
AbstractCube, a data type defined as a function from RegionFeatures space to
relational tables. AbstractCube provides a logical form of data for HoCA
operators and their compositions to operate on to analyze the data. This
function-as-data modeling allows us to simultaneously capture a space of
non-uniform tables on the co-domain of the function, and region space structure
on the domain of the function. We describe two HoCA operators, cube crawling
and cube join, which are cube-to-cube transformations (i.e., higher-order
functions). Cube crawling explores a region subspace, and outputs a cube
mapping regions to signal vectors. Cube join, in turn, allows users to meld
information in different cubes, which is critical for composition. The cube
crawling interface introduces two novel features: (1) Region Analysis Models
(RAMs), which allows one to program and organize analysis on a set of data
features into a module. (2) Multi-Model Crawling, which allows one to apply
multiple models, potentially on different feature sets, during crawling. These
two features, together with cube join and a rich RAM library, allows us to
construct succinct HoCA programs to capture a wide variety of data-insight
problems in system monitoring, experimentation analysis, and business
intelligence. HoCA poses a rich algorithmic design space, such as optimizing
crawling performance leveraging region space structure, optimizing cube join
performance, and physical designs of cubes. We describe several cube crawling
implementations leveraging different foundations (an in-house relational query
engine, and Apache Beam), and evaluate their performance characteristics.
Finally, we discuss avenues in extending the framework, such as devising more
useful HoCA operators.Comment: Establishing core concepts of HoC
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