They may also be presented in announcements of the thesis examination. Most readers who encounter your abstract in a bibliographic database or receive an email announcing your research presentation will never retrieve the full Is Generation Z the Laziest Generation Ever? or attend the presentation.
An abstract is not merely an introduction in the sense of a preface, preamble, or what is an abstract page in a thesis paper organizer that prepares the reader for the thesis.
In addition to that function, it must be capable of substituting for the whole thesis when there is insufficient time and space for the full text. Size and Structure Currently, the maximum sizes for abstracts submitted to Canada’s National Archive are words Masters thesis and words Doctoral dissertation.
“Subtype of Autism: Developmental Verbal Dyspraxia”
To preserve visual coherence, you may wish to limit the abstract for your doctoral dissertation to one double-spaced page, about words. The structure of the abstract should mirror the structure of the whole thesis, and should represent all its major elements. For example, if your thesis has five chapters introduction, literature review, methodology, results, conclusionthere should be one or more sentences assigned to summarize each chapter.
Clearly Specify Your Research Questions As in the thesis itself, your research questions are critical in ensuring that the abstract is coherent and logically structured. They form the skeleton to which other elements adhere. They should be presented near the beginning of the abstract. There is only room for one to three questions. On the other hand, we define external meta information as information that can be inferred about a document, but is not contained good essay writing it.
Examples of external meta information include things like reputation of the source, update frequency, quality, popularity or usage, and citations.
How to Write Your Thesis
Not only are the possible sources of what is an abstract page in a thesis paper meta information what is an abstract page in a thesis paper, but the things essay on tuberculosis a global health emergency are being measured vary many orders of magnitude as well. For example, compare the usage information from a major homepage, like Yahoo’s which currently receives millions of page views every day with an obscure historical article which might receive one view every ten years.
Clearly, these two items must be treated very differently by a search engine. Another big difference between the web and traditional well controlled collections is that there is virtually no control over what people can put on the web. Couple this flexibility to publish anything with the enormous influence of search engines to route traffic and companies which deliberately manipulating search engines for profit become a serious problem.
This problem that has not been addressed in traditional closed information retrieval systems. Also, it is interesting to note that metadata efforts have largely failed with web search engines, because any text on the page which is not directly represented to the Domain and range homework is abused to manipulate search engines.
There are what is an abstract page in a thesis paper numerous companies which specialize in manipulating search engines for profit. Then, there is some in-depth descriptions of important data structures. Finally, the major applications: High Level Google Architecture 4.
Further sections will discuss the applications and data structures not mentioned in this section. In Google, the web Mr. Know All Essay – EssaysForStudent.com downloading of web pages is done by several distributed crawlers.
The web pages that are fetched are then sent to the storeserver. The storeserver then compresses and stores the web pages into a repository.
The indexing function is performed by the indexer and the sorter.
The Common core algebra 1 unit 3 lesson 6 homework answers performs a number of functions. It reads the repository, uncompresses the documents, and parses them. Each document is converted into a set of word occurrences called hits. The hits record the word, position in document, an approximation of font size, and capitalization.
The indexer distributes these hits into a set of essay year 6 This file contains enough information to determine where each link points from and to, and the text of the link.
It puts the anchor text into the forward index, associated with the docID that the anchor points to. It also generates a database of links which are pairs of docIDs.
The links database is used to compute PageRanks for all the documents. The sorter takes the barrels, which are sorted by docID this is a simplification, see Section 4.
This is done in place so that little temporary space is needed for this operation. The sorter also produces a list of wordIDs apple tv case study offsets into the inverted index. A program called DumpLexicon takes this list together with the lexicon produced by the indexer and generates a new lexicon to be used by the searcher.
The searcher is run by a web server and uses the lexicon built by DumpLexicon what is an abstract page in a thesis paper with the inverted index and the PageRanks to answer queries. Although, CPUs and bulk input output rates have improved dramatically over the years, a disk seek still requires about 10 ms to complete.
Google is designed to avoid disk seeks whenever possible, and this has had a considerable influence on the design of the data structures.
- When you start reading about a topic, you should open a spread sheet file, or at least a word processor file, for your literature review.
- The research focus i.
- In many cases, all of the interesting and relevant data can go in the thesis, and not just those which appeared in the journal.
- We are looking for a critical analysis.
- Sometimes you will be able to present the theory ab initio, but you should not reproduce two pages of algebra that the reader could find in a standard text.
- So write something, even if it is just a set of notes or a few paragraphs of text that you would never show to anyone else.
- The final section in the paper is a recommendation section.
- Try to step back mentally and take a broader view of the problem.
The allocation among multiple file systems is handled automatically. The BigFiles package also handles allocation and deallocation of file descriptors, since the operating systems do not provide enough for our needs. BigFiles also support rudimentary compression options. Each page is compressed using zlib see RFC The choice of compression technique is a tradeoff between speed and compression ratio.
We chose zlib’s speed over a significant improvement in compression offered by bzip. The compression rate of bzip was approximately 4 to 1 on the repository as compared to zlib’s 3 to 1 compression. In the repository, the documents are what is an abstract page in a thesis paper one after the other and are prefixed by docID, length, and URL as can be seen in Figure 2. The repository requires no other data structures to be used in order to access it.
This helps with data consistency and makes development much easier; we can rebuild all the other data Creative … from only the repository and a file which lists crawler errors. The information stored in each entry includes the current document status, a pointer into the repository, a document checksum, and various statistics.
If the document has been crawled, my first camera essay also contains a pointer into a variable width file called docinfo which contains its URL and title. This design decision was driven by the desire to have a reasonably compact data structure, and the ability to fetch a record in one disk seek during a search Additionally, there is a file which is used to convert URLs into docIDs.
URLs may be converted into docIDs in batch by doing a merge with this file. This batch mode of update is crucial because otherwise we must perform one seek for every link which assuming one disk would take more than a month for our million link dataset. One important change from earlier systems is that the lexicon can fit in memory for a reasonable price.
In the current implementation we can keep the lexicon in memory on a machine with MB of main memory. The current lexicon contains 14 million words though some rare words were not added to the lexicon. It is implemented in two parts — a list of the words concatenated together but separated by nulls and a hash table of pointers. For what is an abstract page in a thesis paper functions, the list of words has what is an abstract page in a thesis paper auxiliary information what is an abstract page in a thesis paper is beyond the scope of this paper to explain fully.
Hit lists account for most of the space used in both the forward and the inverted indices. Because of this, it is important to represent them as efficiently as possible. We considered several alternatives for encoding position, font, and capitalization — simple encoding a triple of integersa compact encoding a hand optimized allocation of bitsand Huffman coding.
In the end we chose a hand optimized compact encoding since it required far less space than the simple encoding and far less bit manipulation than Huffman coding. The details of the hits are shown in Figure 3.
Our compact encoding uses two bytes for every hit. There are two types of hits: Fancy hits include hits occurring in a URL, title, anchor text, or meta tag. Plain hits include everything else. A plain hit consists of a capitalization bit, font size, and 12 bits of word position in a document all positions higher than argumentative essay about technology in education labeled Font size is represented relative to the rest of the document using three bits only 7 values are actually used because is the flag that signals a fancy hit.
A fancy hit consists of a capitalization bit, the font size research paper smoking public places of the document because when searching, you do not want to rank otherwise identical documents differently just because one of the documents is in a larger font.
Forward and Reverse Indexes and the Lexicon The length of a hit list is stored before the hits themselves. To save space, the length of the hit list is combined with the wordID in the forward index and the docID in the inverted index. This limits it to 8 and 5 bits respectively there are some tricks which allow 8 bits to be borrowed from the wordID.
Ipsis thesis format the length is longer than would fit in that many bits, an escape code is used in those bits, and the next two bytes contain the actual length.
It is stored in a number of barrels we used Each barrel holds a range of wordID’s. If a document contains words that fall into a particular barrel, the docID is recorded into the barrel, followed by a list of wordID’s with hitlists which correspond to those words.
This scheme requires slightly more storage because of duplicated docIDs but the difference is very small for a reasonable number of buckets and saves considerable time and coding complexity in the final indexing phase done by the sorter. Furthermore, instead of storing actual wordID’s, we store each wordID as a relative difference from the minimum wordID that falls into the barrel the wordID is in.
This way, we can use just 24 bits for the wordID’s in the unsorted barrels, leaving 8 bits for the hit list length. For every valid wordID, the lexicon contains a pointer into the barrel that wordID falls into. It points to a doclist of docID’s together with their corresponding hit lists. This doclist represents all the occurrences of that word in all documents. An important issue is in what order the docID’s should appear in the doclist.
One simple solution is to store them sorted by docID. This allows for quick merging of different doclists for multiple word queries. Another option is to store them sorted by a ranking of the occurrence of the word in each document. This makes answering one word queries trivial and makes it likely that the answers to multiple word queries are near the start.
However, merging is much more difficult. Also, this makes development much more difficult in that a change to the ranking function requires a rebuild of the index. We chose a compromise between these options, keeping two sets of inverted barrels — one set for hit lists which include what is an abstract page in a thesis paper or anchor hits and another set for all hit lists.
This way, we check the first set of barrels first and if there are not enough matches within those barrels we check the larger ones. There are tricky performance and reliability issues and even more importantly, there are social issues. Crawling is the most fragile application since it involves interacting with hundreds of thousands of web servers and various name servers which are all beyond the control of the system.
In order to scale to hundreds of millions of web pages, Google has a what is an abstract page in a thesis paper distributed crawling system. Both the URLserver and the crawlers are implemented in Python.
Each crawler keeps what is an abstract page in a thesis paper connections open at once. This is necessary to retrieve web pages at a fast enough pace. At peak speeds, the system can crawl over web pages per second using four crawlers.
This amounts to roughly K per second of data. A major performance stress is DNS lookup. Each of the hundreds of connections can be in a number of different states: These factors make the crawler a complex component of the system.
It uses asynchronous IO to manage events, and a number of queues to move page fetches from state to state. It turns out that running a crawler which connects to more than half a million servers, and generates tens of millions of log entries generates a fair amount of email and phone persuasive essay examples seen.
Almost daily, we receive an email something like, “Wow, you looked at a lot of pages from my web site. How did you like it? Also, because of the huge amount of data involved, unexpected things will happen. effective essay writing example, our system tried to crawl an online game.
This resulted in lots of garbage messages in the middle of their game! It turns out this was an easy problem to fix.
But this problem had not come up until we had downloaded tens of millions of pages. Because of the immense variation in web pages and servers, it is virtually impossible to test a crawler without running it on large part of the Internet. Invariably, there are hundreds of obscure problems which may only occur on one page out of the whole web and cause the crawler to what is an abstract page in a thesis paper, or worse, cause unpredictable or incorrect behavior.
Systems which access what is an abstract page in a thesis paper parts of the Internet need to be what is an abstract page in a thesis paper to be very robust and carefully tested. Since large complex systems such as crawlers will invariably cause problems, there needs to be significant resources devoted to reading the email and solving these problems as they come up.
These range from typos in HTML tags to kilobytes of zeros in the middle of a tag, non-ASCII characters, HTML tags nested hundreds deep, and a great variety of other errors that challenge anyone’s imagination to come up with equally creative ones. For maximum speed, instead of using YACC to generate a CFG parser, we use flex to generate a lexical analyzer which we outfit with its own stack.
Developing this parser which runs at a reasonable speed and is very robust involved a fair amount singkeekaya.com work. Indexing Documents into Barrels — After each document is parsed, it is encoded into a number of barrels.
Every word is converted into a wordID by using an in-memory hash table — the lexicon. New additions to the lexicon hash table are what is an abstract page in a thesis paper to a file. Once the words are converted into wordID’s, their occurrences in the current document are translated into hit lists and are written into the forward barrels.
The main difficulty with parallelization of the indexing phase is that the lexicon needs to be shared. Instead of sharing the lexicon, we took the approach of writing a log of all the extra words that were not in a what is an abstract page in a thesis paper lexicon, which we fixed at 14 million words.
That way multiple indexers can run in parallel and then the small log file of extra words can be processed by one final indexer. Sorting — In order to generate the inverted index, the sorter takes thesis on the shadow lines by amitav ghosh of the forward barrels and sorts it by wordID to produce an inverted barrel for title and anchor hits and a full text inverted barrel. This process happens one barrel at a time, thus requiring little temporary storage.
Also, we parallelize the sorting phase to use as many machines as we have simply by running multiple sorters, which can process different buckets at the same time.
Since the barrels don’t fit into main memory, the sorter further subdivides them into baskets which do fit into memory based on wordID and docID. Then the sorter, loads each basket into memory, sorts it and writes its contents Famous Thinkers Essay – 1103 Palabras the short inverted barrel and the what is an abstract page in a thesis paper inverted barrel.
Many of the large commercial search engines seemed to have made great progress in terms of efficiency. Therefore, we have focused more on quality of search in our research, although we believe our solutions are scalable to commercial volumes with a bit more effort. The google query evaluation process is show in Figure 4. Seek to the start of the doclist in the short barrel for every word. Scan through the doclists until there is a document that matches all the search terms.
Compute the rank of that document for the query. If we are in the short barrels and at the end of any doclist, seek to the start of the doclist in the full barrel for every word and go to step 4.
If we are not at the end of any doclist go to step 4. Sort the documents that have matched by rank and return the top k. Google Query Evaluation To put a limit on response time, once a certain number currently 40, of matching documents are found, the searcher automatically goes to step 8 in Figure 4. This means that it is possible that sub-optimal results would be returned.
We are currently investigating other ways to solve this problem. In the past, we sorted the hits according to PageRank, which seemed to improve the situation. Every hitlist includes position, font, and capitalization information. Additionally, we factor in hits from anchor text and the PageRank of the document. Combining all of this information into a rank is difficult.
We designed our ranking function so that no particular factor can have too much influence. First, consider the simplest case — a single word query. In order to rank a document with a single word query, Google looks at that document’s hit demoshiksha.000webhostapp.com for that word.
Google considers each hit to be one of several different types title, anchor, URL, plain text large font, plain text small font, The type-weights make up a vector indexed by type.