Data Structure Analysis is a context-sensitive pointer analysis which identifies data structures on the heap and their important properties (such as type safety). Automatic pool Allocation uses the results of Data Structure Analysis to segregate dynamically allocated objects on the heap, giving control over the layout of the data structure in memory to the compiler. Based on these two foundation techniques, this thesis describes several performance improving optimizations for pointer-intensive programs. First, automatic pool Allocation itself provides important locality improvements for the program. Once the program is pool allocated, several pool-specific optimizations can be performed to reduce inter-object padding and pool overhead. Second, we describe an aggressive technique, automatic pointer Compression, which reduces the size of pointers on 64-bit targets to 32-bits or less, increasing effective cache capacity and memory bandwidth for pointer-intensive programs. This thesis describes the approach, analysis, and transformation of programs with macroscopic techniques, and evaluates the net performance impact of the transformations. Finally, it describes a large class of potential applications for the work in fields such as heap safety and reliability, program understanding, distributed computing, and static garbage collection. The "book dark form" is useful if you plan to print this out.
Theme includes the issue of the role of is audit in this area, and describes a sample audit procedure for the chosen type of it project.) Audit Roles in Cyber Preparedness (The thesis will cover the definitions and types of cyber threats and tools available for. 2018 your feedback for this page copyright (C) University of Economics in Prague webmaster. Providing high performance for pointer-intensive programs on modern architectures is an increasingly difficult problem for compilers. Pointer-intensive programs are often bound by memory latency and cache performance, but traditional approaches to these problems usually fail: pointer-intensive programs are often highly-irregular and the compiler has little control over the layout of heap allocated objects. This thesis presents a new class of techniques named Macroscopic Data. Structure Analyses and Optimizations which is a new approach to the problem of analyzing trunk and optimizing pointer-intensive programs. Instead of analyzing individual load/store operations or structure definitions, this approach identifies, analyzes, and transforms entire memory structures as a unit. The foundation of the approach is an analysis named Data Structure Analysis and a transformation named Automatic pool Allocation.
It covers social networks Facebook, twitter, Instagram, and others, and wonders how it is possible to use modern technology for the benefit of education.) Social media in business (Practically oriented topic that ideally on a casestudy describes the current use of new media. Nowadays remains increasingly less room for privacy and intimacy. Is there need to redefine privacy?) New media other topics Potančok, martin health.0 (Opportunities and benefits of it trends in healthcare health.0. This topic is oriented on a concept of healthcare which includes components (sensors, bio-sensors, systems and cybernetic extension) and the Internet (Internet of Things, iot). The digital connection allows improvements in diagnostic, therapeutic and nursing procedures, incl. Final topic must be consulted with the tutor). Řepa, václav business Activity monitoring Business Process Engineering svatá, vlasta auditing it projects (IT project management as well project portfolio management are the important topics within Enterprise governance.
HyperSpy: multi-dimensional data analysis toolbox — hyperSpy
Find here, ism master Thesis Archive. List of mt topics/ supervisors (it is possible to suggest your engineering own topic after making an arrangement with your supervisor) Berka, petr Data mining project (The thesis should describe an application of a selected data mining system on real data and real problem. The thesis should follow the crisp-dm methodology and thus cover the whole data mining process.) doucek, petr ict and Economy (Impact of ict on macro and micro economic aspects. Topics could be for example following: impact of ict on labour productivity, analysis of ict market in selected countries or regions, analysis of legal frame in relation to ict business in selected country. Final topic must be consulted with tutor.) Internet of Thinks new Business Models (This topic is oriented on changes of business models using new information technology.
Main questions are oriented on: secutiry aspects and potential solving of security threats, new business aspects that brings with it the new technology, possibility of applying this technology in life and business.) kučera, jan Increasing maturity of project and portfolio management processes with Big Data. A framework or a set of recommendations for effective use of Big Data analysis for improvement of the project and portfolio management processes should be proposed and evaluated. Approach literature and schedule need to be discussed with the supervisor prior to signing-up for this topic.) Open (government) Data value chains (Goal of this thesis is to analyse how value is generated through Open (government) Data publication and re-use. A framework for measuring this value should be proposed as well as guidelines for its application in a selected country. Barriers to its application should also be analysed. Approach and schedule need to be discussed with the supervisor prior to signing-up for this topic.) novotný, ota pavlíček, antonín Social media in education (This topic describes the possibilities, advantages and disadvantages of using social media in education.
In the 4th semester write and finalise the. Submit your mt both in printed form in the ism office and in the online form into Insis at latest until 10th may 2018/ 30th June 2018. Defend your mt within your final state examination (at least one week before the state exam you will get the supervisor´s and opponent´s reviews). The state exam takes place on 11th June 2018/ 27th August 2018. Mt formal aspects, the total lenght of the mt is 60 to 80 norm pages excluding annexes, printed in A4 format. Recommended line spacing.15, typeface a proportional serif font of size 12 pt (norm/ standard page: 250-280 words/.800 characters, including spaces).
Pages are numbered consecutively except the front page. Do not forget to cite sources of all third-party information (citation, paraphrases). Structure of the mt: cover page, cover page (hard cover of your work: you can choose whatever color of the buckram you wish the shop to print bind it is nearby the university). Title page, declaration, acknowledgement (not compulsory abstract and keywords (all on one page). Content, text of the thesis (Introduction, Theoretical part, Practical part, conclusions). List of abbreviations (if applicable list of tables and charts, list of references (literature) and other sources. Annexes, find here, how to write thesis presentation, all mt are to be checked by the anti-plagiarism software.
Importance of Data Analysis in Research dissertation India
He is author or coauthor of textbooks which have appeared in ten languages. visit my blog for more ebooks and also can connect to rss. Search, you're browsing at: ism homepage current Students / Master´s Thesis, related pages, the study programme dubai is closed by the final state examanition, including defence of the master´s thesis (MT). Mt formal aspects, mt- archive, list of mt topics/ supervisors, mT deadline overview. Choose the mt topic and your thesis supervisor (before the 3rd semester) list of topics find here (it is possible to suggest your own topic after making an arrangement with your supervisor). Register into Insis for the diploma seminar, there you get detailed instruction how movie to work at your. In the 3rd semester gather information, sources and researches.
Random numbers and the monte carlo method. Statistical distributions (binomial, gauss, poisson). Audience, the book is conceived both as an introduction and as a work of reference. In particular it addresses itself to students, scientists and practitioners in science and engineering as a help in the analysis of their data in laboratory courses, in working for bachelor or master degrees, in thesis work, in research and professional work. The book is concise, but gives a sufficiently rigorous mathematical treatment of practical statistical methods for data analysis; it can be of great use to all who are involved with data analysis. Serves as a nice reference about guide for any scientist interested in the fundamentals of data analysis on the computer. This lively and erudite treatise covers the theory of the main statistical tools and their practical applicationsa first rate university textbook, and good background material for the practicing physicist. The author, siegmund Brandt is Emeritus Professor of Physics at the University of siegen. With his group he worked on experiments in elementary-particle physics at the research centers desy in Hamburg and cern in Geneva in which the analysis of the experimental data plays an important role.
Multivariate Analyses, non-parametric Analysis, time series, statistical Process Controls. Matrices, distributions and Statistical Analysis, analyses about Regression Solutions and Their coefficients with Squares Method. Logistic Regression, weighted Regression, system and Techniques of Simultaneous Equations. Time series Analysis, vector Autoregression, cointegration. Causality tests Granger, simulations, valuation Methods, contingency table. Curve fitting, double Identification Tests, descriptive statistics, experimental Design Prediction. Meta analysis, quality control, multivariate william ratio analysis, mixed Models.
M: Software for Data Analysis: Programming with
Professional Consulting Services, chi-square, correlation, clustering, variance. Factor, equation essay models, spss, mitap, wilcoxon Test, man Whitney test. Kruskal Wallis Test, dependent and Independent Sample test, reliability test. Determination of Segmentation Relational Rules, statistical analyses with statistics, segmentation with Statistics. Data manipulation Methods, mathematical Modeling, time series Analysis with Statistics, modeling. Econometrics Analysis, input and Output of Data, edit and Manipulation on Data. Arithmetic, graphical Structuring of Data, main Statistical evaluations, regression.