1,777 research outputs found
Streamability of nested word transductions
We consider the problem of evaluating in streaming (i.e., in a single
left-to-right pass) a nested word transduction with a limited amount of memory.
A transduction T is said to be height bounded memory (HBM) if it can be
evaluated with a memory that depends only on the size of T and on the height of
the input word. We show that it is decidable in coNPTime for a nested word
transduction defined by a visibly pushdown transducer (VPT), if it is HBM. In
this case, the required amount of memory may depend exponentially on the height
of the word. We exhibit a sufficient, decidable condition for a VPT to be
evaluated with a memory that depends quadratically on the height of the word.
This condition defines a class of transductions that strictly contains all
determinizable VPTs
A Grammatical Inference Approach to Language-Based Anomaly Detection in XML
False-positives are a problem in anomaly-based intrusion detection systems.
To counter this issue, we discuss anomaly detection for the eXtensible Markup
Language (XML) in a language-theoretic view. We argue that many XML-based
attacks target the syntactic level, i.e. the tree structure or element content,
and syntax validation of XML documents reduces the attack surface. XML offers
so-called schemas for validation, but in real world, schemas are often
unavailable, ignored or too general. In this work-in-progress paper we describe
a grammatical inference approach to learn an automaton from example XML
documents for detecting documents with anomalous syntax.
We discuss properties and expressiveness of XML to understand limits of
learnability. Our contributions are an XML Schema compatible lexical datatype
system to abstract content in XML and an algorithm to learn visibly pushdown
automata (VPA) directly from a set of examples. The proposed algorithm does not
require the tree representation of XML, so it can process large documents or
streams. The resulting deterministic VPA then allows stream validation of
documents to recognize deviations in the underlying tree structure or
datatypes.Comment: Paper accepted at First Int. Workshop on Emerging Cyberthreats and
Countermeasures ECTCM 201
Streaming Property Testing of Visibly Pushdown Languages
In the context of language recognition, we demonstrate the superiority of
streaming property testers against streaming algorithms and property testers,
when they are not combined. Initiated by Feigenbaum et al., a streaming
property tester is a streaming algorithm recognizing a language under the
property testing approximation: it must distinguish inputs of the language from
those that are -far from it, while using the smallest possible
memory (rather than limiting its number of input queries).
Our main result is a streaming -property tester for visibly
pushdown languages (VPL) with one-sided error using memory space
.
This constructions relies on a (non-streaming) property tester for weighted
regular languages based on a previous tester by Alon et al. We provide a simple
application of this tester for streaming testing special cases of instances of
VPL that are already hard for both streaming algorithms and property testers.
Our main algorithm is a combination of an original simulation of visibly
pushdown automata using a stack with small height but possible items of linear
size. In a second step, those items are replaced by small sketches. Those
sketches relies on a notion of suffix-sampling we introduce. This sampling is
the key idea connecting our streaming tester algorithm to property testers.Comment: 23 pages. Major modifications in the presentatio
Social media analytics: a survey of techniques, tools and platforms
This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an âexplosionâ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing
Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources
Apache Calcite is a foundational software framework that provides query
processing, optimization, and query language support to many popular
open-source data processing systems such as Apache Hive, Apache Storm, Apache
Flink, Druid, and MapD. Calcite's architecture consists of a modular and
extensible query optimizer with hundreds of built-in optimization rules, a
query processor capable of processing a variety of query languages, an adapter
architecture designed for extensibility, and support for heterogeneous data
models and stores (relational, semi-structured, streaming, and geospatial).
This flexible, embeddable, and extensible architecture is what makes Calcite an
attractive choice for adoption in big-data frameworks. It is an active project
that continues to introduce support for the new types of data sources, query
languages, and approaches to query processing and optimization.Comment: SIGMOD'1
Lower Bounds for Multi-Pass Processing of Multiple Data Streams
This paper gives a brief overview of computation models for data stream
processing, and it introduces a new model for multi-pass processing of multiple
streams, the so-called mp2s-automata. Two algorithms for solving the set
disjointness problem wi th these automata are presented. The main technical
contribution of this paper is the proof of a lower bound on the size of memory
and the number of heads that are required for solvin g the set disjointness
problem with mp2s-automata
Digital television applications
Studying development of interactive services for digital television is a leading edge area of work as there is minimal research or precedent to guide their design. Published research is limited and therefore this thesis aims at establishing a set of computing methods using Java and XML technology for future set-top box interactive services. The main issues include middleware architecture, a Java user interface for digital television, content representation and return channel communications.
The middleware architecture used was made up of an Application Manager, Application Programming Interface (API), a Java Virtual Machine, etc., which were arranged in a layered model to ensure the interoperability. The application manager was designed to control the lifecycle of Xlets; manage set-top box resources and remote control keys and to adapt the graphical device environment. The architecture of both application manager and Xlet forms the basic framework for running multiple interactive services simultaneously in future set-top box designs.
User interface development is more complex for this type of platform (when compared to that for a desktop computer) as many constraints are set on the look and feel (e.g., TV-like and limited buttons). Various aspects of Java user interfaces were studied and my research in this area focused on creating a remote control event model and lightweight drawing components using the Java Abstract Window Toolkit (AWT) and Java Media Framework (JMF) together with Extensible Markup Language (XML).
Applications were designed aimed at studying the data structure and efficiency of the XML language to define interactive content. Content parsing was designed as a lightweight software module based around two parsers (i.e., SAX parsing and DOM parsing). The still content (i.e., text, images, and graphics) and dynamic content (i.e., hyperlinked text, animations, and forms) can then be modeled and processed efficiently.
This thesis also studies interactivity methods using Java APIs via a return channel. Various communication models are also discussed that meet the interactivity requirements for different interactive services. They include URL, Socket, Datagram, and SOAP models which applications can choose to use in order to establish a connection with the service or broadcaster in order to transfer data.
This thesis is presented in two parts: The first section gives a general summary of the research and acts as a complement to the second section, which contains a series of related publications.reviewe
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