22 research outputs found
Enhancing Deep Learning Models through Tensorization: A Comprehensive Survey and Framework
The burgeoning growth of public domain data and the increasing complexity of
deep learning model architectures have underscored the need for more efficient
data representation and analysis techniques. This paper is motivated by the
work of (Helal, 2023) and aims to present a comprehensive overview of
tensorization. This transformative approach bridges the gap between the
inherently multidimensional nature of data and the simplified 2-dimensional
matrices commonly used in linear algebra-based machine learning algorithms.
This paper explores the steps involved in tensorization, multidimensional data
sources, various multiway analysis methods employed, and the benefits of these
approaches. A small example of Blind Source Separation (BSS) is presented
comparing 2-dimensional algorithms and a multiway algorithm in Python. Results
indicate that multiway analysis is more expressive. Contrary to the intuition
of the dimensionality curse, utilising multidimensional datasets in their
native form and applying multiway analysis methods grounded in multilinear
algebra reveal a profound capacity to capture intricate interrelationships
among various dimensions while, surprisingly, reducing the number of model
parameters and accelerating processing. A survey of the multi-away analysis
methods and integration with various Deep Neural Networks models is presented
using case studies in different application domains.Comment: 34 pages, 8 figures, 4 table
Enabling Ubiquitous OLAP Analyses
An OLAP analysis session is carried out as a sequence of OLAP operations applied to multidimensional cubes. At each step of a session, an operation is applied to the result of the previous step in an incremental fashion. Due to its simplicity and flexibility, OLAP is the most adopted paradigm used to explore the data stored in data warehouses. With the goal of expanding the fruition of OLAP analyses, in this thesis we touch several critical topics. We first present our contributions to deal with data extractions from service-oriented sources, which are nowadays used to provide access to many databases and analytic platforms. By addressing data extraction from these sources we make a step towards the integration of external databases into the data warehouse, thus providing richer data that can be analyzed through OLAP sessions. The second topic that we study is that of visualization of multidimensional data, which we exploit to enable OLAP on devices with limited screen and bandwidth capabilities (i.e., mobile devices). Finally, we propose solutions to obtain multidimensional schemata from unconventional sources (e.g., sensor networks), which are crucial to perform multidimensional analyses
Efficient Structure-aware OLAP Query Processing over Large Property Graphs
Property graph model is a semantically rich model for real-world applications that represent their data as graphs, e.g., communication networks, social networks, financial transaction networks. On-Line Analytical Processing (OLAP) provides an important tool for data analysis by allowing users to perform data aggregation through different combinations of dimensions. For example, given a Q&A forum dataset, in order to study if there is a correlation between a poster's age and his or her post quality, one may ask what is the average age of users grouped by the post score. Another example is that, in the field of music industry, it may be interesting to ask what total sales of records are with respect to different music companies and years so as to conduct a market activity analysis. Surprisingly, current graph databases do not efficiently support OLAP aggregation queries. In most cases, such queries are transformed to a sequence of join operations, and the system computes everything from scratch. For example, Neo4j, a state-of-art graph database system, processes each OLAP query in two steps. First, it expands the nodes and edges that satisfy the given query constraint. Then it performs the aggregation over all the valid substructures returned from the first step. However, in data warehousing workloads, it is common to have repeated queries from time to time. Computing everything from scratch would be highly inefficient. Materialization and view maintenance techniques developed in traditional RDBMS have proved to be efficient for processing OLAP workloads. Following the generic materialization methodology, in this thesis we develop a structure-aware cuboid caching solution to efficiently support OLAP aggregation queries over property graphs. Structure-aware means that our solution takes both heterogeneous attributes and graph topological information into consideration. The essential idea is to precompute and materialize some views based on statistics of history workload, such that future query processing can be accelerated.
We implement a prototype system on top of Neo4j. Empirical studies over real-world property graphs show that, with a reasonable space cost constraint, our solution on average achieves 15-30x speedup over native Neo4j in time efficiency
Dwarf: A Complete System for Analyzing High-Dimensional Data Sets
The need for data analysis by different industries, including
telecommunications, retail, manufacturing and financial services, has
generated a flurry of research, highly sophisticated methods and
commercial products. However, all of the current attempts are haunted
by the so-called "high-dimensionality curse"; the complexity of space
and time increases exponentially with the number of analysis
"dimensions". This means that all existing approaches are limited
only to coarse levels of analysis and/or to approximate answers with
reduced precision. As the need for detailed analysis keeps
increasing, along with the volume and the detail of the data that is
stored, these approaches are very quickly rendered unusable. I have
developed a unique method for efficiently performing analysis that is
not affected by the high-dimensionality of data and scales only
polynomially -and almost linearly- with the dimensions without
sacrificing any accuracy in the returned results. I have implemented a
complete system (called "Dwarf") and performed an extensive
experimental evaluation that demonstrated tremendous improvements over
existing methods for all aspects of performing analysis -initial
computation, storing, querying and updating it.
I have extended my research to the "data-streaming" model where
updates are performed on-line, exacerbating any concurrent analysis
but has a very high impact on applications like security, network
management/monitoring router traffic control and sensor networks. I
have devised streaming algorithms that provide complex statistics
within user-specified relative-error bounds over a data stream. I
introduced the class of "distinct implicated statistics", which is
much more general than the established class of "distinct count"
statistics. The latter has been proved invaluable in applications such
as analyzing and monitoring the distinct count of species in a
population or even in query optimization. The "distinct implicated
statistics" class provides invaluable information about the
correlations in the stream and is necessary for applications such as
security. My algorithms are designed to use bounded amounts of memory
and processing -so that they can even be implemented in hardware for
resource-limited environments such as network-routers or sensors- and
also to work in "noisy" environments, where some data may be flawed
either implicitly due to the extraction process or explicitly
Storage and aggregation for fast analytics systems
Computing in the last decade has been characterized by the rise of data- intensive scalable computing (DISC) systems. In particular, recent years have wit- nessed a rapid growth in the popularity of fast analytics systems. These systems exemplify a trend where queries that previously involved batch-processing (e.g., run- ning a MapReduce job) on a massive amount of data, are increasingly expected to be answered in near real-time with low latency. This dissertation addresses the problem that existing designs for various components used in the software stack for DISC sys- tems do not meet the requirements demanded by fast analytics applications. In this work, we focus specifically on two components:
1. Key-value storage: Recent work has focused primarily on supporting reads with high throughput and low latency. However, fast analytics applications require that new data entering the system (e.g., new web-pages crawled, currently trend- ing topics) be quickly made available to queries and analysis codes. This means that along with supporting reads efficiently, these systems must also support writes with high throughput, which current systems fail to do. In the first part of this work, we solve this problem by proposing a new key-value storage system – called the WriteBuffer (WB) Tree – that provides up to 30× higher write per- formance and similar read performance compared to current high-performance systems.
2. GroupBy-Aggregate: Fast analytics systems require support for fast, incre- mental aggregation of data for with low-latency access to results. Existing techniques are memory-inefficient and do not support incremental aggregation efficiently when aggregate data overflows to disk. In the second part of this dis- sertation, we propose a new data structure called the Compressed Buffer Tree (CBT) to implement memory-efficient in-memory aggregation. We also show how the WB Tree can be modified to support efficient disk-based aggregation.Ph.D
Towards Personalized and Human-in-the-Loop Document Summarization
The ubiquitous availability of computing devices and the widespread use of
the internet have generated a large amount of data continuously. Therefore, the
amount of available information on any given topic is far beyond humans'
processing capacity to properly process, causing what is known as information
overload. To efficiently cope with large amounts of information and generate
content with significant value to users, we require identifying, merging and
summarising information. Data summaries can help gather related information and
collect it into a shorter format that enables answering complicated questions,
gaining new insight and discovering conceptual boundaries.
This thesis focuses on three main challenges to alleviate information
overload using novel summarisation techniques. It further intends to facilitate
the analysis of documents to support personalised information extraction. This
thesis separates the research issues into four areas, covering (i) feature
engineering in document summarisation, (ii) traditional static and inflexible
summaries, (iii) traditional generic summarisation approaches, and (iv) the
need for reference summaries. We propose novel approaches to tackle these
challenges, by: i)enabling automatic intelligent feature engineering, ii)
enabling flexible and interactive summarisation, iii) utilising intelligent and
personalised summarisation approaches. The experimental results prove the
efficiency of the proposed approaches compared to other state-of-the-art
models. We further propose solutions to the information overload problem in
different domains through summarisation, covering network traffic data, health
data and business process data.Comment: PhD thesi