Browsing by Subject "data structures"

Nowadays power consumption is a hot and actual domain. Efficient energy consumption allows you to use resources from the environment wisely and, moreover, switch to alternative energy sources where it is possible. This thesis is aimed at analyzing elevator’s power consumption data (average per every 5 minutes and 1 hour). The data has been gathered for several years, so it is a time series. This thesis includes review of time series models, which then can be used for the consequent analysis. Main directions are forecasting power consumption, capturing trends and anomalies. In addition, time series data may also be used for calculating average power consumption for each elevator inside the elevator group. As an outcome, spread of the power consumption across 4 elevators inside the elevator group may be seen. One of the thesis’ goals is to check whether it is even or not.

This thesis is a review of articles focusing on software assisted floor plan design for architecture. I group the articles into optimization, case based design, and machine learning, based on their use of prior examples. I then look into each category and further classify articles based on dimensions relevant to their overall approach. Case based design was a popular research field in the 1990s and early 2000s when several large research projects were conducted. However, since then the research has slowed down. Over the past 20 years, optimization methods to solve architectural floor plans have been researched extensively using a number of different algorithms and data models. The most popular approach is to use a stochastic optimization method such as a genetic algorithm or simulated annealing. More recently, a number of articles have investigated the possibility of using machine learning on architectural floor plans. The advent of neural networks and GAN models, in particular, has spurred a great deal of new research. Despite considerable research efforts, assisted floor plan design has not found its way into commercial applications. To aid industry adoption, more work is needed on the integration of computational design tools into the existing design workflows.

The volume of data generated by high-throughput DNA sequencing has grown to a magnitude that leads to substantial computational challenges in storing and searching the data. To tackle this problem, various computational methodologies have been developed in recent years to space-efficiently index collections of data sets and enable efficient searches. One of the most recent indexing methods, Spectral Burrows-Wheeler Transform (SBWT), presents all distinct k-mers of a DNA sequence using only 4 bits and a small additional space for the rank data structures per k-mer. In addition to being space-efficient, it also enables k-mer membership queries in linear time relative to k, and constant time relative to the number of distinct k-mers in the sequence. The queries rely on rank queries over bit vectors. Experiments run on a single CPU thread have shown that in one second, hundreds of thousands of k-mer membership queries can be performed over SBWT. By parallelizing the queries on a CPU, it is possible to execute millions of queries per second. However, Graphic Processing Units (GPUs) have much more parallelization potential. The main contribution of the thesis is an implementation of the k-mer membership queries over SBWT with GPU computing. Optimizing the queries to be performed on a GPU made it possible to perform over a billion queries per second. Furthermore, the thesis presents a new enhancement for the queries over SBWT called presearching, which doubles the speed of the original SBWT search query. The rank query needed for the membership queries is implemented using space-efficient poppy rank data structures, and its derivative cumulative-poppy data structure which is one of the contributions of the thesis.