TraTSA: A Transprecision Framework for Efficient Time Series Analysis

Time series analysis (TSA) comprises methods for extracting information in domains as diverse as medicine, seismology, speech recognition and economics. Matrix Profile (MP) is the state-of-the-art TSA technique, which provides the most similar neighbor to each subsequence of the time series. However, this computation requires a huge amount of floating-point (FP) operations, which are a major contributor ( 50%) to the energy consumption in modern computing platforms. In this sense, Transprecision Computing has recently emerged as a promising approach to improve energy efficiency and performance by using fewer bits in FP operations while providing accurate results. In this work, we present TraTSA, the first transprecision framework for efficient time series analysis based on MP. TraTSA allows the user to deploy a high-performance and energy-efficient computing solution with the exact precision required by the TSA application. To this end, we first propose implementations of TraTSA for both commodity CPU and FPGA platforms. Second, we propose an accuracy metric to compare the results with the double-precision MP. Third, we study MP’s accuracy when using a transprecision approach. Finally, our evaluation shows that, while obtaining results accurate enough, the FPGA transprecision MP (i) is 22.75 faster than a 72-core server, and (ii) the energy consumption is up to 3.3 lower than the double-precision executions.This work has been supported by the Government of Spain under project PID2019-105396RB-I00, and Junta de Andalucia under projects P18-FR-3433 and UMA18-FEDERJA-197. Funding for open access charge: Universidad de Málaga / CBUA

Tree-Mining: Understanding Applications and Challenges

Tree-mining is an essential system of techniques and software technologies for multi-level and multi-angled operations in databases. Pertaining to the purview of this manuscript, several applications of various sub-techniques of tree mining have been explored. The current write-up is aimed at investigating the major applications and challenges of different types and techniques of tree mining, as there have been patchy and scanty investigations so far in this context. To accomplish these tasks, the author has reviewed some of the latest and most pertinent research articles of the last two decades to investigate the titled aspects of this technique

Mining and analysis of real-world graphs

Networked systems are everywhere - such as the Internet, social networks, biological networks, transportation networks, power grid networks, etc. They can be very large yet enormously complex. They can contain a lot of information, either open and transparent or under the cover and coded. Such real-world systems can be modeled using graphs and be mined and analyzed through the lens of network analysis. Network analysis can be applied in recognition of frequent patterns among the connected components in a large graph, such as social networks, where visual analysis is almost impossible. Frequent patterns illuminate statistically important subgraphs that are usually small enough to analyze visually. Graph mining has different practical applications in fraud detection, outliers detection, chemical molecules, etc., based on the necessity of extracting and understanding the information yielded. Network analysis can also be used to quantitatively evaluate and improve the resilience of infrastructure networks such as the Internet or power grids. Infrastructure networks directly affect the quality of people\u27s lives. However, a disastrous incident in these networks may lead to a cascading breakdown of the whole network and serious economic consequences. In essence, network analysis can help us gain actionable insights and make better data-driven decisions based on the networks. On that note, the objective of this dissertation is to improve upon existing tools for more accurate mining and analysis of real-world networks --Abstract, page iv

A bitwise clique detection approach for accelerating power graph computation and clustering dense graphs

Graphs are at the essence of many data representations. The visual analytics over graphs is usually difficult due to their size, which makes their visual display challenging, and their fundamental algorithms, which are often classified as NP-hard problems. The Power Graph Analysis (PGA) is a method that simplifies networks using reduced representations for complete subgraphs (cliques) and complete bipartite subgraphs (bicliques), in both cases with edge reductions. The benefits of a power graph are the preservation of information and its capacity to show essential information about the original network. However, finding an optimal representation (maximum edges reduction) is also an NPhard problem. In this work, we propose BCD, a greedy algorithm that uses a Bitwise Clique Detection approach to finding power graphs. BCD is faster than competing strategies and allows the analysis of bigger graphs. For the display of larger power graphs, we propose an orthogonal layout to prevent overlapping of edges and vertices. Finally, we describe how the structure induced by the power graph is used for clustering analysis of dense graphs. We demonstrate with several datasets the results obtained by our proposal and compare against competing strategies.Os grafos são essenciais para muitas representações de dados. A análise visual de grafos é usualmente difícil devido ao tamanho, o que representa um desafio para sua visualização. Além de isso, seus algoritmos fundamentais são frequentemente classificados como NP-difícil. Análises dos grafos de potência (PGA em inglês) é um método que simplifica redes usando representações reduzidas para subgrafos completos chamados cliques e subgrafos bipartidos chamados bicliques, em ambos casos com una redução de arestas. Os benefícios da representação de grafo de potência são a preservação de informação e a capacidade de mostrar a informação essencial sobre a rede original. Entretanto, encontrar uma representação ótima (a máxima redução de arestas possível) é também um problema NP-difícil. Neste trabalho, propomos BCD, um algoritmo guloso que usa um abordagem de detecção de bicliques baseado em operações binarias para encontrar representações de grafos de potencia. O BCD é mas rápido que as estratégias atuais da literatura. Finalmente, descrevemos como a estrutura induzida pelo grafo de potência é utilizado para as análises dos grafos densos na detecção de agrupamentos de nodos

Teak: A Novel Computational And Gui Software Pipeline For Reconstructing Biological Networks, Detecting Activated Biological Subnetworks, And Querying Biological Networks.

As high-throughput gene expression data becomes cheaper and cheaper, researchers are faced with a deluge of data from which biological insights need to be extracted and mined since the rate of data accumulation far exceeds the rate of data analysis. There is a need for computational frameworks to bridge the gap and assist researchers in their tasks. The Topology Enrichment Analysis frameworK (TEAK) is an open source GUI and software pipeline that seeks to be one of many tools that fills in this gap and consists of three major modules. The first module, the Gene Set Cultural Algorithm, de novo infers biological networks from gene sets using the KEGG pathways as prior knowledge. The second and third modules query against the KEGG pathways using molecular profiling data and query graphs, respectively. In particular, the second module, also called TEAK, is a network partitioning module that partitions the KEGG pathways into both linear and nonlinear subpathways. In conjunction with molecular profiling data, the subpathways are ranked and displayed to the user within the TEAK GUI. Using a public microarray yeast data set, previously unreported fitness defects for dpl1 delta and lag1 delta mutants under conditions of nitrogen limitation were found using TEAK. Finally, the third module, the Query Structure Enrichment Analysis framework, is a network query module that allows researchers to query their biological hypotheses in the form of Directed Acyclic Graphs against the KEGG pathways

Data semantic enrichment for complex event processing over IoT Data Streams

This thesis generalizes techniques for processing IoT data streams, semantically enrich data with contextual information, as well as complex event processing in IoT applications. A case study for ECG anomaly detection and signal classification was conducted to validate the knowledge foundation

Applications of motif discovery in biological data

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2007.Includes bibliographical references (p. 437-458).Sequential motif discovery, the ability to identify conserved patterns in ordered datasets without a priori knowledge of exactly what those patterns will be, is a frequently encountered and difficult problem in computational biology and biochemical engineering. The most prevalent example of such a problem is finding conserved DNA sequences in the upstream regions of genes that are believed to be coregulated. Other examples are as diverse as identifying conserved secondary structure in proteins and interpreting time-series data. This thesis creates a unified, generic approach to addressing these (and other) problems in sequential motif discovery and demonstrates the utility of that approach on a number of applications. A generic motif discovery algorithm was created for the purpose of finding conserved patterns in arbitrary data types. This approach and implementation, name Gemoda, decouples three key steps in the motif discovery process: comparison, clustering, and convolution. Since it decouples these steps, Gemoda is a modular algorithm; that is, any comparison metric can be used with any clustering algorithm and any convolution scheme. The comparison metric is a data-specific function that transforms the motif discovery problem into a solvable graph-theoretic problem that still adequately represents the important similarities in the data.(cont.) This thesis presents the development of Gemoda as well as applications of this approach in a number of different contexts. One application is an exhaustive solution of an abstraction of the transcription factor binding site discovery problem in DNA. A similar application is to the analysis of upstream regions of regulons in microbial DNA. Another application is the identification of protein sequence homologies in a set of related proteins in the presence of significant noise. A quite different application is the discovery of extended local secondary structure homology between a protein and a protein complex known to be in the same structural family. The final application is to the analysis of metabolomic datasets. The diversity of these sample applications, which range from the analysis of strings (like DNA and amino acid sequences) to real-valued data (like protein structures and metabolomic datasets) demonstrates that our generic approach is successful and useful for solving established and novel problems alike. The last application, of analyzing metabolomic datasets, is of particular interest. Using Gemoda, an appropriate comparison function, and appropriate data handling, a novel and useful approach to the interpretation of metabolite profiling datasets obtained from gas chromatography coupled to mass spectrometry is developed.(cont.) The use of a motif discovery approach allows for the expansion of the scope of metabolites that can be tracked and analyzed in an untargeted metabolite profiling (or metabolomic) experiment. This new approach, named SpectConnect, is presented herein along with examples that verify its efficacy and utility in some validation experiments. The beginning of a broader application of SpectConnect's potential is presented as well. The success of SpectConnect, a novel application of Gemoda, validates the utility of a truly generic approach to motif discovery. By not getting bogged down in the specifics of a type of data and a problem unique to that type of data, a broader class of problems can be addressed that otherwise would have been extremely difficult to handle.by Mark Philip-Walter Styczynski.Ph.D

Protein-Ligand Binding Affinity Directed Multi-Objective Drug Design Based on Fragment Representation Methods

Drug discovery is a challenging process with a vast molecular space to be explored and numerous pharmacological properties to be appropriately considered. Among various drug design protocols, fragment-based drug design is an effective way of constraining the search space and better utilizing biologically active compounds. Motivated by fragment-based drug search for a given protein target and the emergence of artificial intelligence (AI) approaches in this field, this work advances the field of in silico drug design by (1) integrating a graph fragmentation-based deep generative model with a deep evolutionary learning process for large-scale multi-objective molecular optimization, and (2) applying protein-ligand binding affinity scores together with other desired physicochemical properties as objectives. Our experiments show that the proposed method can generate novel molecules with improved property values and binding affinities