Data questions

Xem 1-20 trên 271 kết quả Data questions
  • In these questions ,you are to classify each problem according to the five fixed answer choices, rather than find s solution to the problem. Each problem consists of a question and two statement. You are to decide whether the information in each statement slone is sufficient to answer the question or if neither is, whether the information in the two statements together is sufficent

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  • The following disscusion of data sufficiency is intended to familiarize you with the most effecient and effective approaches to the kinds of problem common to data sufficiency. The problem on the sample questions you will encounted in this section of the GMAT remember that it is the problem- solving strategy is important, not the specific details of a particular question

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  • 1. Which of the following correctly discribes the characteristics of switched and routed data flows ? A Switches create a single collision domain and a single broadcast domain, Routers provide seperate collision domains. B Switches create seperate collision domains and seperate broadcast domains, Routers provide seperate collision domains and seperate broadcast domain as well.

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  • This book is about coding interview question of software and Internet companies. It covers five key factors which determine performance of candidates: (1) the basics of programming languages, data structures and algorithms, (2) approaches to writing code with high quality, (3) tips to solve difficult problems

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  • If you were to ask a random sampling of people what data analysis is, most would say that it is the process of calculating and summarizing data to get an answer to a question. In one sense, they are correct. However, the actions they are describing represent only a small part of the process known as data analysis

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  • The first question that I ask an environmental science student who comes seeking my advice on a data analysis problem is Have you looked at your data? Very often, after some beating around the bush, the student answers, Not really. The student goes on to explain that he or she loaded the data into an analysis package provided by an advisor, or downloaded off the web, and it didn’t work. The student tells me, Something is wrong with my data! I then ask my second question: Have you ever used the analysis package with a dataset that did work? After some further beating around the bush,...

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  • This book and e-book will provide all that you need to know to pass the Microsoft Silverlight 4 development (70-506) exam. Includes a comprehensive set of test questions and answers The layout and content of the book matches that of the skills measured by the exam closely, which makes it easy to focus your learning and maximize your study time where you need improvement.

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  • What are your organization’s policies for generating and using huge datasets full of personal information? This book examines ethical questions raised by the big data phenomenon, and explains why enterprises need to reconsider business decisions concerning privacy and identity. Authors Kord Davis and Doug Patterson provide methods and techniques to help your business engage in a transparent and productive ethical inquiry into your current data practices.

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  • Data networks developed as a result of business applications that were written for microcomputers. At that time microcomputers were not connected as mainframe computer terminals were, so there was no efficient way of sharing data among multiple microcomputers. It became apparent that sharing data through the use of floppy disks was not an efficient or cost-effective manner in which to operate businesses.

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  • How do the 3.1 billion A, C, G and T letters of the human genome compare to those of a chimp or a mouse? What do the paths that millions of visitors take through a web site look like? With Visualizing Data, you learn how to answer complex questions like these with thoroughly interactive displays. We're not talking about cookie-cutter charts and graphs. This book teaches you how to design entire interfaces around large, complex data sets with the help of a powerful new design and prototyping tool called "Processing"....

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  • Question 1. Reorder the following efficiencies from the smallest to the largest: a. 2n3 + n5 b. 2000 c. 4n+1 d. n4 e. (n-1)! f. nlog2(n) g. 2klogk(n) (k is a predefined constant) Solution: Efficiency: a measure of amount of time for an algorithm to execute (Time Efficiency) or a measure of amount of memory needed for an algorithm to execute (Space Efficiency).

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  • We will refer to embeddings providing a guarantee akin to that of Lemma 1.1 as JL- embeddings. In the last few years, such embeddings have been used in solving a variety of problems. The idea is as follows. By providing a low-dimensional representation of the data, JL-embeddings speed up certain algorithms dramatically, in particular algorithms whose run-time depends exponentially in the dimension of the working space. (For a number of practical problems the best-known algorithms indeed have such behavior.

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  • The conducted analysis reveals that only the UK National Survey matches the data needs in order to conduct a comprehensive scenario analysis for the EDV recharge profiles. On the other hand, the German NTS has a similar level of detail as the UK NTS but does not include each individual’s trips for an entire week and misses details for parking (where and how long cars remain parked during the day). The remaining national travel surveys present the data only at aggregated level. This kind of data can be used to identify different travel behaviors across different conditions (e.

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  • Natural language questions have become popular in web search. However, various questions can be formulated to convey the same information need, which poses a great challenge to search systems. In this paper, we automatically mined 5w1h question reformulation patterns from large scale search log data.

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  • In this paper we address the problem of question recommendation from large archives of community question answering data by exploiting the users’ information needs. Our experimental results indicate that questions based on the same or similar information need can provide excellent question recommendation. We show that translation model can be effectively utilized to predict the information need given only the user’s query question.

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  • Before you start working your way through this book, you may ask yourself— Why analyze data? This is an important, basic question, and it has several compelling answers. The simplest need for data analysis arises most naturally in disciplines addressing phenomena that are, in all likelihood, inherently nondeterministic (e.g., feelings and psychology or stock market behavior). Since such fields of knowledge are not governed by known fundamental equations, the only way to generalize disparate observations into expanded knowledge is to analyze those observations.

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  • We present a graph-based semi-supervised learning for the question-answering (QA) task for ranking candidate sentences. Using textual entailment analysis, we obtain entailment scores between a natural language question posed by the user and the candidate sentences returned from search engine. The textual entailment between two sentences is assessed via features representing high-level attributes of the entailment problem such as sentence structure matching, question-type named-entity matching based on a question-classifier, etc.

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  • This paper is concerned with the problem of question search. In question search, given a question as query, we are to return questions semantically equivalent or close to the queried question. In this paper, we propose to conduct question search by identifying question topic and question focus. More specifically, we first summarize questions in a data structure consisting of question topic and question focus. Then we model question topic and question focus in a language modeling framework for search.

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  • This paper introduces the concepts of asking point and expected answer type as variations of the question focus. They are of particular importance for QA over semistructured data, as represented by Topic Maps, OWL or custom XML formats. We describe an approach to the identification of the question focus from questions asked to a Question Answering system over Topic Maps by extracting the asking point and falling back to the expected answer type when necessary.

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  • In this paper, we analyze the impact of different automatic annotation methods on the performance of supervised approaches to the complex question answering problem (defined in the DUC-2007 main task). Huge amount of annotated or labeled data is a prerequisite for supervised training. The task of labeling can be accomplished either by humans or by computer programs. When humans are employed, the whole process becomes time consuming and expensive.

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