Log is a file that records the events which happens while an operating system or software runs [1]. It may include any activity such as information about a simple keystroke, the complete record of communication between two machines, system errors, inter-process communication, update events, server activities, client sessions, browsing history, etc. Logs provide a good insight into various states of a system at any instant and their analytical and statistical study can manage systems and mine useful knowledge about a user on various aspects. Log data is voluminous, growing at a very fast rate, with varying structure across various applications, usages, servers, etc. It possesses the key characteristics of the Big Data which include volume, velocity, variety and value.
Analytical study of logs support accurate interpretation of the current state, prediction of upcoming state, and suggest certain reactive measures in a scenario. With such a diverse and rich lot of information, statistical analysis will easily monitor the system performance and take proactive measures to improve it without human intervention. A screenshot in Figure-1 showing 195 log files on a Windows system can give an estimate how diverse and rich information does they contain.
Logs can be classified into various categories based on the type of activity they monitor, the source type, the type of information they reveal. Few such classifications are depicted in Figure-2.
Each log has its own structure and parameters of presenting information but the common fact they share is the rate of growth of data is almost comparable in each case.
A lot of statistical work had already been done on log analysis in past years. Application of time series analysis to transaction log...
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...a mining are Anomaly Detection, Machine Learning, Clustering, Classification, Regression, Summarization etc.
Key directions in data mining are:
a. Feature Extraction: Looks for most extreme examples of certain phenomenon and represent data by those examples [10]. It may be similarity based or frequent item sets.
b. Statistical Modeling: Decide about which distribution pattern a data set follows. It eases the descriptive analysis and provides foundation for predictive analysis.
c. Machine Learning: Uses data to train the algorithm and proves effective when we have little knowledge of what we are looking for.
d. Computational Approaches: It considers the algorithms complexity aspect of data mining. It may include approximation or specifics.
e. Summarization: Provides an overview of data based on certain techniques like clustering, cumulative probability concepts etc.
A friendship is not all they have together, Lennie and George have dreams. Lennie and George have worked up the idea of owning their own piece of land together. Lennie wants to tend the rabbits (Steinbeck 11) and George just wants to be his own boss (Steinbeck 14). The only problem with their dream is that it is unrealistic. They cannot buy land to tend and just go days without tending it because they do not want to. Like many traveling farm hands during the 1930s, George and Lennie think they could work up enough money to buy their own place and not give a “hoot” about anyone but their selves. Although their dream is unattaina...
In Of Mice and Men, George and Lenny are migrant workers in an area where they aren’t taken seriously. This is because other migrant workers that came around the farm wasted their monthly pay on drinking and gambling. George and Lenny have different plans though, they want a permanent home to live on, instead of traveling around to find work and leaving once Lenny screws up. They are treated like the drinking and gambling migrant workers throughout the book, because that’s exactly what those other workers had on their minds. During the Great Depression, this was what many had on their minds, but they could never get it because they couldn’t find work, or the work they found didn’t pay well enough. Perhaps, in the book, some migrant workers
Statistical Induction- is based on statistical information, it predicts something will happen with numerical probability.
A major benefit to using transaction logs is that this is data already collected and waiting to ...
b) An example of where Dr. Rogers used “analysis” was when the excavation site was flooded after the rainfall. In order to proceed, Dr. Rogers had to analyze the new site and figure out the best way to go about removing the water from it (Rogers
o The terms of the classification tell us what the individuals in that class have in common.
recognition or both. According to Aristotle an effective plot device, and a very commonly used device
Chapter 12 introduces the reader to the true definition of statistics, without scaring them half to death. The book breaks statistics down in two parts: descriptive and inferential. The type that is dealt with in this chapter is descriptive statistics. The simple definition of descriptive statistics are that they are just numbers in different forms, for example, percentages, numerals, fractions, and decimals. The book gives an example of a grade point average being a descriptive statistic.
The University generally requires formal papers to use section headings to establish structure. For this short paper, section headings are not required, but they are encouraged.
It is the branch of statistics that deals with the collection, presentation, description, analysis and interpretation of a dataset.
Lepide Event Log Manager (LELM) has an edge over the traditional and native Windows Event Viewer because of its next-gen features. Being a centralized solution, it allows you to manage the event logs of multiple computers in the same or different domains at a common platform. At scheduled intervals, LELM will collect the logs of added computers automatically in two ways - with an agent and without an agent. The former allows the better parsing of the events, but it will install an agent program on the target computer, whereas the latter doesn’t need any further installation. All the logs are stored permanently for long-term usage in a proprietary database. In this blog post, we’ll discuss how to monitor the event logs using Lepide Event Log Manager.
to prepare these data and the possibility of data collection errors will make the data preparation
Statistics refers to the use of numerical information in everyday life to calculate facts and figures in limitless circumstances such as, batting averages, market share, and changes in the stock market. In addition, statistics refers to the scientific collecting, classifying, summarizing, organizing, analyzing, and interpreting numerical data. Statistics involves describing data sets and drawing conclusions based on sampling about the data sets (McClave, Benson & Sincich, 2011). Statistics are divided into two areas: descriptive statistics and inferential statistics.
Big data is a concept that has been misunderstood therefore I will be writing this paper with the intentions of thoroughly discussing this technological concept and all its dimensions with regard to what constitutes big data and how the term came about. The rapid innovations in Information Technology have brought about the realisation of big data. The concept of big data is complex and has different connotations but I intend to clarify its functions. Big data refers to the concept of a collection of large and complex amounts of data that are found extremely difficult to notate or even process by most on-hand devices and database technologies.
Humans can expand their knowledge to adapt the changing environment. To do that they must “learn”. Learning can be simply defined as the acquisition of knowledge or skills through study, experience, or being taught. Although learning is an easy task for most of the people, to acquire new knowledge or skills from data is too hard and complicated for machines. Moreover, the intelligence level of a machine is directly relevant to its learning capability. The study of machine learning tries to deal with this complicated task. In other words, machine learning is the branch of artificial intelligence that tries to find an answer to this question: how to make computer learn?