Comparative Analysis of Rank Techniques

Comparative Analysis of Rank Techniques Abstract There is paramount web data available in the form of web pages on the World Wide Web (WWW). So whenever a user makes a query, a lot of search results having different web links corresponding to a user’s query are generated. Out of which only some are relevant while the rest are irrelevant. The relevancy of a web page is calculated by search engines using page ranking algorithms. Most of the page ranking algorithm use web structure mining and web content mining to calculate the relevancy of a web page. Most of the ranking algorithms which are given in the literature are either link or content oriented which do not consider user usage trends. The Algorithm called Page Rank Algorithm was introduced by Google in beginning. It was considered a standard page rank because as no other algorithm of page rank was in existence. Later extensions of page rank algorithm were incorporated along with different variations like considering weights as well as visits of links. This paper presents the comparison among original page rank algorithm as well as its various variations. Keywords: inlinks, outlinks, search engine, web mining, World Wide Web (WWW), PageRank, Weighted page rank, VOL I. Introduction World Wide Web is a vast resource of hyperlinked and a variety of information including text, image, audio, video and metadata. It is anticipated that WWW has expanded by about 2000% since its progression and is doubling in magnitude with a gap of six to ten months. With the swift expansion of information on the WWW and mounting requirements of users, it is becoming complicated to manage web information and comply with the user needs. So users have to employ some information retrieval techniques to find, extract, filter and order the desired information. The technique used filters the web page according to query generated by the user and create an index. This indexing is related to the rank of web page. Lower the index value, higher will be the rank of the web page. 1. Data Mining over Web 1.1 Web Mining Data mining, which facilitates the knowledge discovery from large data sets by extracting potentially new useful patterns in the form of human understandable knowledge and structuring the same, can also be applied over the web. The application being named Web Mining thus becomes a technique for extracting useful information from a large, unstructured, heterogeneous data store. Web mining is quite a immense area with dozens of developments and technological enhancements. 1.2. Web Mining Categories According to literature, there are three categories of web mining: Web Content Mining (WCM), Web Structure Mining (WSM) and Web Usage Mining (WUM) WCM includes the web page information. In it, the actual content pages whether semi structured hypertext or multimedia information are used for searching purposes. WSM uses the central part linkage that flows through the entire web. The linkage of web content is called hyperlink. This hyperlinked structure is used for ranking the retrieved web pages on the basis of query generated by the user. WUM returns the dynamic results with respect to users’ navigation. This methodology uses the server logs ( the logs that are created during user navigation via searching. WUM is also called as Web Log Mining because it extracts knowledge from usage logs. 1.2 Page Rank Algorithm (By Google) This is the original PageRank algorithm. It was postulated by Lawrence Page and Sergey Brin. The formula is: where is the PageRank of page A is the PageRank of pages Ti which link to page A is the number of outbound links on page Ti d is a damping factor having value between 0 and 1. The PageRank algorithm is used to determine the rank of a web page individually. This algorithm is not meant to rank a web site. Moreover, the PageRank of a page say A, is recursively defined by the PageRanks of those pages which link to page A. The PageRank of pages which link to page A does not influence the PageRank of page A consistently. In PageRank algorithm, the PageRank of a page T is always weighted by the number of outbound links C(T) on page T. It means, more outbound links a page T has, the less will page A benefit from a link to it on page T. The weighted PageRank of pages Ti is then added up. But an additional inbound link for page A will always increase page As PageRank. In the end, the sum of the weighted PageRanks of all pages is multiplied with a damping factor d which can be set between 0 and 1. Thus, the extend of PageRank benefit for a page by another page linking to it is reduced. They deem PageRank as a genre of user behaviour, where a surfer clicks on links at random irrespective of content. The random surfer visits a web page with a certain probability which is solely given by the number of links on that page. Thus, one pages PageRank is not completely passed on to a page it links to, but is divided by the number of links on the page. So, the probability for the random surfer reaching one page is the sum of probabilities for the random surfer following links to this page. Now, this probability is diminish by the damping factor d. Sometimes, user doesnot move straight to the links of a page, instead the user jumps to some other page randomly. This probability for the random surfer is calculated by the damping factor d (also called as degree of probability having value between 0 and 1). Regardless of inbound links, the probability for the random surfer jumping to a page is always (1-d), so a page has always a minimum PageRank. A revised version of the PageRank Algorithm is given by Lawrence Page and Sergey Brin. In this algorithm, the PageRank of page A is given as where N is the total number of all pages on the web. This revised version of the algorithm is basically equivalent the original one. Regarding the Random Surfer Model, this version is the actual probability for a surfer reaching that page after clicking on many links. The sum of all page ranks of all pages will be one by calculating the probability distribution of all web pages. But, these versions of the algorithm do not differ fundamentally from each other. A PageRank which has been calculated by using the second version of the algorithm has to be multiplied by the total number of web pages to get the according PageRank that would have been calculated by using the first version. 1.3 Dangling Nodes A node is called a dangling node if it does not contain any out-going link, i.e., if the out-degree is zero. The hypothetical web graph taken in this paper is having a dangling node i.e. Node D. II Research background Brin and Page (Algorithm: Google Page Rank) The authors came up with an idea to use link structure of the web to calculate rank of web pages. This algorithm is used by Google based on the results produced by keyword based search. It works on the principle that if a web page has significant links towards it, then the links of this page to other pages are also considered imperative. Thus, it depends on the backlinks to calculate the rank of web pages. The page rank is calculated by the formula given in equation 1. (1) Where u represents a web page and represents the page rank of web pages u and v respectively is the set of web pages pointing to u represents the total numbers of outlinks of web page v and c is a factor used for normalization Original PageRank algorithm was modified considering that all users donot follow direct links on web data. Thus, the modified formula for calculating page rank is given in equation 2. (2) Where d is a dampening factor which represent the probability of user using direct links and it can be set between 0 and 1. Wenpu Xing and Ali Ghorbani (Algorithm: Weighted Page Rank) The authors gave this method by extending standard PageRank. It works on the theory that if a page is vital, it has many inlinks and outlinks. Unlike standard PageRank, it does not equally distribute the page rank of a page among its outgoing linked pages. The page rank of a web page is divided among its outgoing linked pages in proportional to the importance or popularity (its number of inlinks and outlinks). , the popularity from the number of inlinks, is calculated based on the number of inlinks of page u and the number of inlinks of all reference pages of page v as given in equation 3. (3) Where and are the number of inlinks of page u and p respectively represents the set of web pages pointed by v. , the popularity from the number of outlinks, is calculated based on the number of outlinks of page u and the number of outlinks of all reference pages of page v as given in equation. 4. (4) Where and are the number of outlinks of page u and p respectively represents the set of web pages pointed by v. The page rank using Weighted PageRank algorithm is calculated by the formula as given in equation 5. (5) Gyanendra Kumar et. al. (Algorithm : Page Rank with Visits of Links (VOL)) This methodology includes the browsing behavior of the user. The prior algorithms were either based on WSM or WCM. But it incluses Page Ranking based on Visits of Links (VOL). It modifies the basic page ranking algorithm by considering the number of visits of inbound links of web pages. It assists to prioritize the web pages on the basis of user’s browsing behavior. Also, the rank values are assigned in proportional to the number of visits of links in this algorithm. The more rank value is assigned to the link which is most visited by user. The Page Ranking based on Visits of Links (VOL) can be calculated by the formula given in equation 6. (6) Where and represent page rank of web pages u and v respectively d is dampening factor B(u) is the set of web pages pointing to u Lu is number of visits of links pointing from v to u TL(v) is the total number of visits of all links from v. Neelam Tyagi and Simple Sharma (Algorithm: Weighted Page Rank Algorithm Based on Number of Visits of Links of Web Page) The authors incorporate Weighted PageRank algorithm and the number of visits of links (VOL). This algorithm consigns more rank to the outgoing links having high VOL. It is based on the inlink popularity ignoring the outlink popularity. In this algorithm, number of visits of inbound links of web pages are taken into consideration in addition the weights of page. The rank of web page using this algorithm can be calculated as given in equation 7. (7) Where represent page rank of web page u and v respectively d is the dampening factor B(u) is the set of web pages pointing to u Lu is number of visits of links pointing from v to u is the total number of visits of all links from v represents the popularity from the number of inlinks of u. Sonal Tuteja (Algorithm: Enhancement in Weighted Page Rank Using Visits of Link (VOL)) The author incorporated i.e. the weight of link(v,u) and calculated based on the number of visits of inlinks of page u. the popularity from the number of visits of outlinks are used to calculate the value of page rank. is the weight of link(v, u) which is calculated based on the number of visits of inlinks of page u and the number of visits of inlinks of all reference pages of page v as given in equation 8. (8) Where and represents the incoming visits of links of page u and p respectively R(v) represents the set of reference pages of page v. is the weight of link(v, u) which is calculated based on the number of visits of outlinks of page u and the number of visits of outlinks of all reference pages of page v as given in equation 9. (9) Where and represents the outgoing visits of links of page u and v respectively R(v) represents the set of reference pages of page v. Now these values are used to calculate page rank using equation (10) (10) Where d is a dampening factor B(u) is the set of pages that point to u WPRVOL (u) and WPRVOL(v) are the rank scores of page u and v respectively represents the popularity from the number of visits of inlinks represents the popularity from the number of visits of outlinks III Numerical analysis of various page rank algorithms To demonstrate the working of page rank, consider a hypothetical web structure as shown below: Figure showing a web graph having three web pages i.e. A, B, C, D Page Rank (By Brin Page) Using equation 2, the ranks for pages A, B, C are calculated as follows: (1) (2) (3) (4) Having value d=0.25, 0.5, 0.85, the page ranks of pages A, B and C become: Dampening Factor PR(A) PR(B) PR(C) PR(D) 0.25 0.9 0.975 1.22 0.99 0.5 0.8 0.9 1.35 0.95 0.85 0.85 0.829 1.53 0.357 From the results, it is concluded that PR(C)> PR(D)> PR(B)> PR(A) 2. Iterative Method of Page Rank It is easy to solve the equation system, to determine page rank values, for a small set of pages, but the web consists of billions of documents and it is not possible to find a solution by inspection method. In iterative calculation, each page is assigned a starting page rank value of 1 as shown in table 1 below. These rank values are iteratively substituted in page rank equations to find the final values. In general, many iterations could be followed to normalize the page ranks. d=0.25 d=0.5 d=0.85 Iteration PR(A) PR(B) PR(C) PR(D) PR(A) PR(B) PR(C) PR(D) PR(A) PR(B) PR(C) PR(D) 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1.25 1 1 1 1.5 1 1 0.5 1.425 0.575 2 0.875 0.97 1.21 0.99 0.875 0.94 1.44 0.97 0.75 0.788 1.46 0.82 3 0.90 0.975 1.22 0.99 0.86 0.93 1.4 0.965 0.77 0.80 1.48 0.83 †¦Ã¢â‚¬ ¦ †¦Ã¢â‚¬ ¦ †¦Ã¢â‚¬ ¦. †¦Ã¢â‚¬ ¦ †¦Ã¢â‚¬ ¦ †¦Ã¢â‚¬ ¦ †¦Ã¢â‚¬ ¦ †¦Ã¢â‚¬ ¦ †¦Ã¢â‚¬ ¦. From the results, it is concluded that PR(C)> PR(D)> PR(B)> PR(A) 3. Page Rank with Visits of Links (VOL) (Gyanendra Kumar) Using equation 6, the ranks for pages A, B, C are calculated as follows: (A)=(1-d)+d((1) (B)=(1-d)+d((2) (C)=(1-d)+d(+(3) (D)=(1-d)+d((4) The intermediate values can be calculated as: Similarly other values after calculation are: 2/3 Having value d=0.25,0.5, 0.85 the page ranks of pages A, B and C become: Dampening Factor PR(A) PR(B) PR(C) PR(D) 0.25 0.83 0.82 1.23 0.818 0.5 0.635 0.606 0.808 0.6 0.85 0.2478 0.22 0.3449 0.1123 From the results, it is concluded that PR(C)> PR(A)> PR(B)> PR(D) 4. Weighted Page Rank (Wenpu Xing and Ali Ghorbani) Using equation 3, the ranks for pages A, B, C are calculated as follows: (C,A).(1) (2) (3) (4) The weights of incoming as well as well as outgoing links can be calculated as: (C,A)= IA/IA+IC = 1/ 1+2 = 1/3 =OA/OA=1 Having value d=0.5, the page ranks of pages A, B and C become: Dampening Factor PR(A) PR(B) PR(C) PR(D) 0.25 0.8526 0.8210 1.2315 0.75 0.5 0.7059 0.6176 1.235 0.5 0.85 0.3380 0.2458 0.6636 0.15 From the results, it is concluded that PR(C)> PR(A)> PR(B)> PR(D) 5. Weighted Page Rank Based on Visits of Link (VOL) (Neelam Tyagi and Simple Sharma) Using equation 7, the ranks for pages A, B, C are calculated as follows: )(1) )(2) (3) (4) The weights of incoming, number of visits of link as well as total number of visits of all links can be calculated as Having value d=0.25, 0.5 0.85, the page ranks of pages A, B and C become: Dampening Factor PR(A) PR(B) PR(C) PR(D) 0.25 0.8061 0.7836 1.015 0.8153 0.5 05981 0.5498 0.8825 0.5916 0.85 0.1734 0.1735 0.3469 0.1994 From the results, it is concluded that PR(C)> PR(D)> PR(A)> PR(B) 5. Enhancement in Weighted Page Rank Using Visits of Link (VOL) (Sonal Tuteja) Using equation 10, the ranks for pages A, B, C are calculated as follows: (1) (2) (3) Intermediate values can be calculated as follows: =IA/IA=1 =OA/OA=1 Having value d=0.25, 0.5, 0.85 the page ranks of pages A, B and C become: Dampening Factor PR(A) PR(B) PR(C) PR(D) 0.25 0.7226 0.7951 1.029 0.75 0.5 0.9557 0.6195 0.9115 0.5 0.85 1.911 0.5561 1.116 0.15 From the results, it is concluded that PR(C)> PR(B)> PR(D)> PR(A) Comparison chart of various Ranking Algorithms Algorithm Page Rank Page Rank with VOL Weighted Page rank WPRV EWPRV

Essay --

There are numerous diverse courses for people to be well versed and trained with respect to particular data and capabilities they need for do the task which are responsible. These approaches include classroom statements with course readings, Pcs, handheld mechanisms, and other electronic approach. In recent years developer try to developed a new training method which is able to create basic work environment for employee. By rapidly moving social order where there is an incredible arrangement of accessible data and information, embracing and applying data at the correct time and opportune spot is required to fundamental productivity in both school and business settings. Augmented Reality (AR) is one technology that breathtakingly moves the area and timing of training. Augmented Reality (AR) is an influential user interface which is consolidating in to the reality and can be in the Pc created data ideal model improving a user's recognition environment. There are many type of VR with different user interfaces and present reality in a characteristic manner, concurrently utilizes the Pc to investigate identified data and to communicate with Pc created virtual objects. 1.1 Problem statement In many training environment using the augmented reality is common and now days many research are done or on-going to develop and improving this area. (Stytz, 2006) The current technology in augmented reality still not in perfect status the reason behind of this idea is lake of connectivity and sensory experience provided by the real world. In other hand the lack of collaborative and situation awareness and the coordination between trainers are another issue in this technology. To Provide better understanding for user of the environment training,... ...s dynamic diversion based learning in science (Klopfer, 2008; Squire & Jan, 2007). AR is additionally thought to help enhancing spatial capacity and mental revolution (Dà ¼nser, Steinbà ¼gl, Kaufmann, & Glà ¼ck, 2006). AR preparing is imperative in situations where the true, physical planet experience is important, e.g. in military operations (Brown, 2006) and in fragile medicinal operations (Yeo et al.,2011) AR system and provisions have been produced and connected to numerous instructive fields, for example AR science, science, math, and history in K-12 trainings and mechanical designing in higher training. moderately not many studies have been defeated the selection and the convenience of AR system and advancements in streamlined preparing. Both military preparing and fabricate in maintenance lives up to expectations are the main pertinent territories of AR to data.

Long Passages

An upsurge of new research suggests that animals have a much higher level of brainpower than previously thought. If animals do have intelligence, how do scientists measure it? Before defining animals' intelligence, scientists defined what is not intelligence. Instinct is not intelligence. It is a skill programmed into an animal's brain by its genetic heritage. Rote conditioning is also not intelligence. Tricks can be learned by repetition, but no real thinking is involved. Cuing, in which animals learn to do or not to do certain things by following outside signals, does not demonstrate intelligence.Scientists believe that insight, the ability to use tools, and communication using human language are all effective measures of the mental ability of animals. (2) When judging animal intelligence, scientists look for insight, which they define as a flash of sudden understanding. When a young gorilla could not reach fruit from a tree, she noticed crates scattered about the lawn near the tre e. She piled the crates into a pyramid, then climbed on them to reach her reward. The gorilla's insight allowed her to solve a new problem without trial and error. (3) The ability to use tools is also an important sign of intelligence.Crows use sticks to pry peanuts out of cracks. The crow exhibits intelligence by showing it has learned what a stick can do. Likewise, otters use rocks to crack open crab shells in order to get at the meat. In a series of complex moves, chimpanzees have been known to use sticks and stalks in order to get at a favorite snack—termites. To make and use a termite tool, a chimp first selects just the right stalk or twig. He trims and shapes the stick, then finds the entrance to a termite mound. While inserting the stick carefully into the entrance, the chimpanzee turns it skillfully to fit the inner tunnels.The chimp attracts the insects by shaking the twig. Then it pulls the tool out without scraping off any termites. Finally, he uses his lips to sk im the termites into his mouth. (4) Many animals have learned to communicate using human language. Some primates have learned hundreds of words in sign language. One chimp can recognize and correctly use more than 250 abstract symbols on a keyboard. These symbols represent human words. An amazing parrot can distinguish five objects of two different types. He can understand the difference between the number, color, and kind of object.The ability to classify is a basic thinking skill. He seems to use language to express his needs and emotions. When ill and taken to the animal hospital for his first overnight stay, this parrot turned to go. â€Å"Come here! † he cried to a scientist who works with him. â€Å"I love you. I'm sorry. Wanna go back? † (5) The research on animal intelligence raises important questions. If animals are smarter than once thought, would that change the way humans interact with them? Would humans stop hunting them for sport or survival? Would anima ls still be used for food, clothing, or medical experimentation?Finding the answer to these tough questions makes a difficult puzzle even for a large-brained, problem-solving species like our own. 1. Crows use sticks to pry peanuts out of cracks. Which of the following is the kind of intelligence or conditioning the situation describes? a. rote learning b. tools c. communication d. instinct 2. The underlined word upsurge, as it is used in the first paragraph of the passage, most nearly means a. an increasingly large amount. b. a decreasing amount. c. a well-known amount. d. an immeasurable amount. 3. The concluding paragraph of this passage infers which of the following? . There is no definitive line between those animals with intelligence and those without. b. Animals are being given opportunities to display their intelligence. c. Research showing higher animal intelligence may fuel debate on ethics and cruelty. d. Animals are capable of untrained thought well beyond mere instinct. 4. According to the passage, which of the following is true about animals communicating through the use of human language? a. Parrots can imitate or repeat a sound. b. Dolphins click and whistle. c. Crows screech warnings to other crows. d.Chimpanzees and gorillas have been trained to use sign language or geometric shapes that stand for words. 5. In paragraph 3, what conclusion can be reached about the chimpanzee's ability to use a tool? a. It illustrates high intelligence because he is able to get his food and eat it. b. It illustrates instinct because he faced a difficult task and accomplished it. c. It illustrates high intelligence because he stored knowledge away and called it up at the right time. d. It illustrates high intelligence because termites are proteinpacked. 6. Which of the following is not a sign of animal intelligence? . shows insight b. cues c. uses tools d. makes a plan Passage 2 (1) Photosynthesis is the process that plants use to convert sunlight into the food that they need to survive and grow. Most plants create some form of sugar from the sunlight, and this sugar is used by the plant as its primary food source. (2) Plants actually need only three things to create this sugar: sunlight, carbon dioxide, and water. The sunlight reacts with the plant's chlorophyll, a green chemical which is used to convert water and carbon dioxide into sugar. (3) As a general rule, photosynthesis occurs in a plant's leaves.The leaf contains chlorophyll, which reacts when sunlight strikes the leaf. It is also the chlorophyll which gives the leaf its typical green color, since photosynthesis absorbs most light rays except green, which are reflected outwards. (4) The process of photosynthesis produces more than just sugar, however. One byproduct of the process is oxygen, which is â€Å"exhaled† by the plant into the atmosphere. In fact, plant photosynthesis is one of the primary sources of oxygen generation on our planet, making plant life essential to almost all living things on earth. 7.According to paragraph 4, which of the following is a byproduct of photosynthesis? a. green pigment b. water c. carbon dioxide d. oxygen 8. The underlined word essential, as used in paragraph 4, most nearly means a. necessary. b. optional. c. fragrant. d. growing. 9. The passage explains that photosynthesis is a. done in the plant stem. b. what makes plants edible. c. the way that plants feed themselves. d. the source of carbon dioxide in the atmosphere. 10. According to the passage, why do plant leaves look green? a. Leaves are actually not green. b. Photosynthesis absorbs all colors except green. .Sugar is green. d. Photosynthesis requires green light to produce sugar. 11. After reading the passage, what can you conclude about photosynthesis? a. Chlorophyll is part of the ozone layer. b. Photosynthesis is the process that feeds a plant. c. Plants need oxygen to survive. d. The roots of a plant provide water. 12. After reading the passage, what can you infer about photosynthesis? a. Human life would not survive without plants. b. Chlorophyll tastes sweet. c. Sunlight has both good and bad effects on plants. d. Too much water can interfere with photosynthesis.Passage 3 1) Abraham Lincoln served as the 16th President of the United States, yet he never went to college. In fact, Lincoln had nearly no formal education whatsoever, attending schools for less than a year throughout his childhood. Yet this should not be construed to mean that Lincoln was ignorant or unlearned; on the contrary, he was one of the most well-read leaders of the time. The fact is that Abraham Lincoln educated himself by studying books of religion, philosophy, and literature, and he continued his voracious reading throughout his life. (2) A lack of public school education did not prevent Lincoln from becoming a great leader.He led the United States through four years of civil war, which threatened to divide the nation into two separate countries. He was a powerful opponent of slavery, and it was largely through his leadership that slavery was abolished in this country. (3) Lincoln's determination to educate himself through diligent reading also led to his reputation as a great orator—and even today his speeches are quoted and studied worldwide.He serves as an example of a great leader—and a great reader. His love of books and good literature enabled Abe Lincoln to rise to world renown. 3. What is the main idea of this passage? a. Abe Lincoln was the 16th President of the United States. b. Abe Lincoln demonstrated the value of reading. c. Abe Lincoln was a Republican. d. Abe Lincoln freed the slaves. 14. Lincoln accomplished all of the following EXCEPT a. preventing the United States from being divided. b. ending slavery. c. becoming a great leader. d. establishing the Lincoln Memorial. 15. A voracious reader is a. likely to become President. b. a person who makes reading a regular habit. c. someone who never went to s chool. d. probably poor. 16.The underlined word construed, as used in paragraph 1, most nearly means a. tormented. b. taken apart. c. unscrewed. d. interpreted. 17. According to the passage, what lesson can be learned from the life of Abraham Lincoln? a. Education is not important. b. All books are worth reading. c. Good reading habits can help a person do great things. d. Politicians are always good role models. 18. How does Lincoln still affect students today? a. His face is on money. b. His speeches are still quoted and studied today. c. People grow beards to look like him. d. Students quit school in hopes of becoming president.

Critical Study of Nutrient Sensing and Root System Architecture (Arabidopsis Thaliana) The WritePass Journal

Critical Study of Nutrient Sensing and Root System Architecture (Arabidopsis Thaliana) 1. Introduction Critical Study of Nutrient Sensing and Root System Architecture (Arabidopsis Thaliana) 1. Introduction2. Problem Statement Root System Architecture   Arabidopsis ThalianaEZ-Rhizo3. Background Survey Development LanguageDrawing packagesJava 2DOpenGLFractal DimensionAlpha BlendingVisualization Uncertainty 4. Proposed ApproachBasic FunctionalityAdditional FunctionalityDesign MethodologyRisk Analysis5. Work Plan Background ResearchRequirements Capture Implementation of the Basic FunctionalityTesting of Basic FunctionalityDesign and Build PrototypesExperiments with PrototypesIntegrate desired Prototypes Testing of Additional FunctionalityComplete and Submit DissertationBibliography Related 1. Introduction In 2004, the United Nations published a report (United Nations, 2004) predicting that the world’s population would steadily increase, year on year, until at least 2050. Between 2004 and 2010 the world’s population increased by 5 million people (PRB, 2004 and PRB 2010), demonstrating the validity of this prediction. Last year, the United Nations (United Nations, 2010) published another report estimating that over 900 million people were still suffering from malnourishment, highlighting an insufficiency within the volume and distribution of food currently being produced.   As a consequence, it has recently been suggested that as the population grows the number of people facing food insecurity will also increase (Den Herder et al, 2010), unless the pressures affecting food production can be alleviated. To increase the volume of food being produced, scientists have advised that at least one of the following objectives has to be achieved (Godfray et al, 2010): Improve the quantity and quality of crop harvesting Identify or create more arable land (land that can be used for growing crops) By increasing the quantity of the food we produce we should theoretically be able to feed the growing population. In recent years the use of fertilizers and pesticides have been used to enhance the quality and quantity of the crops however use of this cannot be increased due to the risks to public health (Den Herder et al, 2010), and so other options must be explored. Scientists are investigating methods with which to promote a plant’s intake of nutrients and to increase their tolerance against hostile environmental conditions such as drought. Another area scientists have been exploring is that of how to convert land that would be considered unsuitable for plant growth (Den Herder et al, 2010). By finding more land on which to cultivate crops, we could generate more produce using our existing techniques. There is extensive research being conducted in both of these areas however this project will only be focussing on the work being contributed by the Plant Science Group.   This Group are part of the Institute of Molecular, Cell and Systems Biology within the College of Medical, Veterinary and Life Sciences at the University of Glasgow.   The Plant Science Group consists of over 50 active scientists, of which Dr Anna Amtmann is currently a Research Group Leader, and their work covers a range of topics from plant nutrition to plants effect on human metabolism.   Within the Plant Science Group, Dr Amtmann’s Research Group is conducting research into the plant Arabidopsis Thaliana, with the aim of â€Å"understanding the molecular mechanisms involved within (Amtmann, 2011)†: Nutrient usage efficiency Salt and drought tolerance Interaction between abiotic and biotic stress To achieve this understanding experimental research is being conducted in a number of areas by her team, and this project aims to support the work within Nutrient Sensing and Root System Architecture. This research involves growing Arabidopsis Thaliana in controlled environmental conditions and analyzing the effect on the plants by monitoring their individual root structures. By comparing root system architectures that have developed in different environmental conditions, specific traits can identified which can lead to the understanding of how plants intake different nutrients and respond to specific stresses. Currently, the team grows hundreds of samples Arabidopsis Thaliana and records information about each of their root system architectures. This results in large volumes of data that require analysis and documentation. Currently the team create graphs to visualise and analyse this data but they have recently advised of the unsuitability of this approach.   This is predominantly due to the limited visualization options of graphs when considering root parameters and the fact that graphs can extremely time-consuming to construct. Therefore, this project aims to develop a system that will allow Dr Amtmann’s Research Group to visualise root system architectures of Arabidopsis Thaliana by using techniques that will manipulate the root structure architectural data into interesting and understandable representations. The following chapters of this proposal aim to document the proposed work that will be undertaken in a master project that will achieve this aim. 2. Problem Statement Due to the biological nature of this project, this section will define a number of key concepts required to understand the work undertaken by Dr Amtmann and her colleagues, and the issues they face, before discussing the intentions of this project. Root System Architecture Dr Amtmann’s team assesses the root system architectures of Arabidopsis Thaliana to understand how environmental conditions affect its development. The root system of a plant is responsible for the intake of water and nutrition (Malamy, 2005), among other essential functions, and so plays an important role within the development of any plant.   The root system normally consists of a main root with a number of lateral branches, which contain a number of root hairs that are responsible for the uptake of nutrients and water (Wikipedia, 2011). The way in which these underlying roots expand and branch is considered to be the root system architecture (RSA) (Figure 1). Depending on the environment in which a plant resides, changes occur to the RSA that can have a significant effect on the water or nutrient uptake of the plant(Armengaud et al, 2009). Therefore by understanding the environmental conditions that promote the growth of a successful RSA we can ensure the development of a healthy plant. Figure 1 – Root Structure Architecture (RSA) taken from â€Å"Root-System Development and Water-Extraction Model Considering Hydrotropism† by D. Tsutsumi et al.   Arabidopsis Thaliana Like many other research projects, Amtmann’s group uses the plant Arabidopsis Thaliana within their investigations. Arabidopsis Thaliana is considered to be a model species within plant science and biology as its genome was sequenced in 2000, meaning that its complete DNA sequence was determined (Wikipedia, 2011).   As the genome is relatively small it is easier to work with and analyse. It is also closely related to a large number of other plants such as (Somerville and Koorneef, 2002) meaning that any finding could be widely applicable. It can also be easily grown in laboratory conditions due to its small size and straightforward growth conditions. As a result of the Arabidopsis Thaliana sequenced genome, scientists can determine which genetic combinations are responsible for certain characteristics of the plant, such as the direction of root growth or number of lateral branches.   Figure 2 highlights some variation in roots of Arabidopsis Thaliana. Figure 2 Different Root Structure Architecture for Arabidopsis taken from unil.ch/dbmv/page36242_en.html Currently Dr Amtmann’s group grows multiple instances of Arabidopsis Thaliana in square agar plates that allow them to take two-dimensional images of the plant roots as it grows.   These images are then examined using a bespoke system called EZ-Rhizo, which was developed by Dr Amtmann’s Research Group, to determine the RSA of the plant. EZ-Rhizo EZ-Rhizo is freely available software that detects and measures the RSA of a plant quickly and accurately (Armengaud et al, 2009) when supplied with an image of a root system. It was developed by Dr Amtmann’s Research Group in conjunction with undergraduate students from l’Ecole Supà ©rieure dIngà ©nieurs en Electrotechnique et Electrique in 2008.   The main function of the system is to measure information about the main root and the lateral roots of a plant, and record this. All numerical values are stored in centimetres.   It then uses this data to derive further parameters such as the apical zone or straightness of the root. The key parameters that are captured by EZ-Rhizo in relation to this project are as follows: Apical Zone, which is the length of the root between the top of the main root and the oldest lateral root Length, which is the total length of the root from the origin of the main root to the tip of it.   (Note that as roots do not grow straight this value may be very large but the root may not have grown that far towards the ground) Vector Length, this is similar to the length however it calculate the shortest possible length of the root starting from the origin of the main root to the tip of it. Straightness, which is the Vector Length, divided by the Length. Angle, which is the degree between the Vector of the root and complete verticality. A full listing of all the parameter capture by EZ-Rhizo can located in the in that’s publication The system was developed for the Microsoft Windows operating system and stores data in a MySQL database. Queries can then be submitted to the database, which will retrieve all of the stored data for a specified root and output it into a Comma Separated Values (CSV) file (Armengaud et al, 2009). Project Objective As previously described, the main method used by Dr Amtmann’s Group to analyse and compare the plant root data, recorded by EZ-Rhizo, is to gather the required CSV files and generate the appropriate graphs in Microsoft Excel. The current methods used by this group can be extremely time consuming and could be subject to error if mislabelling on a graph occurs or if a value within a CSV file is misread. As the team work with large quantities of data there is a high probability of this occurring.   Also, the format of the results could also inhibit certain comparisons, as the visualization of the roots is limited to the graph format. Therefore the aim of this project is to design and implement a new system that will enable Dr Amtmann’s Research Group to overcome these difficulties. The proposed system will allow them to analyse and visualise their root data, collected from EZ-Rhizo, in a more flexible and reliable manner. The basic functionality of this system will be to create a realistic visualization of a root based on the supplied parameters. This will provide the foundation for developing alternative visualizations but it will also allow the researchers to understand the data that EZ-Rhizo is recording. As the system will re-create the root system architecture based on only the values provided by EZ-Rhizo, the re-creations will highlight which characterises of the root can be displayed. Therefore, once the visualizations have been created it may show that more root data is required to be captured by EZ-Rhizo in order to for the root system architecture to be properly documented.   This verification could not be achieved from a graph of the values or from viewing the original root structure image that is supplied to EZ-Rhizo. Once the basic functionality of this system is operational it can be enhanced, by the use of prototypes and experimentation, to find suitable techniques that allow for a meaningful representation of more than one root. For example, the average value of each parameter could be determined for a set of roots and the system could draw the average root. Alternatively given the parameters for a set of roots, the system may be able to estimate a root visualization that had all of those parameters altered by a set value and so produce a theoretical root.   Estimations of this nature would not be possible using the group’s current graphical techniques. In summary, the objective of this project is to implement a system that will create interesting and realistic visualizations of plants root system architectures that will be used by Dr Anna Amtmann and her Research Group in their experiments. 3. Background Survey Development Language One of the functional requirements that was supplied by Dr Anna Amtmann’s Research Group during the initial discussions of this project was that they require this system to be multi-platform, unlike EZ-Rhizo, which was specifically designed for Microsoft Windows Operating Systems. As a result of this requirement and the author’s previous development experience, the Java development language has been selected for this project.   As Java is a cross-platform language that be run anywhere, as long as the Java Virtual Machine (JVM) is present, it meet the requirement set by the Research Group.   Also, as a main priority of this project is to develop and experiment with the greatest number of prototypes, it is advisable to reduce the learn curve that would be required in understanding a new language and so as Java is the developers strongest language it has been selected. Drawing packages Java 2D The Java 2D is a freely available drawing framework that allows for the construction of two-dimensional images (Wikipedia, 2011) and is part of the core Java Standard Edition Application Programming Interfaces (APIs) developed by Sun Microsystems, and now maintained by Oracle (Oracle, 2011).  Ã‚   The key strengths of this API are that it supports the drawing of basic shapes such as rectangles and ellipses but also basic lines, which is required for this project. Also, it will export any images created into well-known formats such as JPEG or GIF (Sun Developer Network, 2011).   As this is a JAVA API there is also a great deal of implementation support provided. OpenGL Open Graphics Language (OpenGL) is a mutli-platform API for creating two-dimensional or three-dimensional graphics (OpenGL, 2011) that is now considered to be an industry standard for graphical development. Currently OpenGL can be used with a number of development languages including Java (JOGL), Python, C++ and C.   As this project will be constructed in Java, JOGL would be the appropriate selection and since the release of Java Standard Edtion 6 in December 2006, JOGL and Java 2D can be used concurrently. However, one limitation of JOGL is that it will add complexity to a if the developers has no previous experience with this API as it currently has 250 different function calls for drawing one image (Wikipedia, 2011). JHotDraw Fractal Dimension One of the key elements of the root visualization is to get it to be as realistic as possible. Now it is likely that the root will be drawn out of straight lines how it will be essential that Alpha Blending Visualization Uncertainty 4. Proposed Approach The suggested approach for this project will involve designing and implementing a basic system that contains all of the functionality required to visualize one root based on the information provided from EZ-Rhizo. Upon successfully implementation of this initial system, a number of prototypes will be created around techniques, such as Alpha Blending, that will manipulate this functionality to draw clusters of roots or alter the initial visualization to provide a different perspective for the user. Any prototype functionality that is considered to be useful, interesting or successful will then be fully integrated into the main system as additional functionality. Basic Functionality It is likely that the new system will send requests directly to the database to retrieve this information rather than obtaining this from CSV files. However this functionality will be discussed with Dr Amtmann’s group during the design phase of the basic system alongside all other functional and non-functional requirements This system will only work with data from EZ-Rhizo at this time and it is assumed that the parameters will be from Arabidopsis Thaliana JAVA – it needs to be multi-platform unlike Ez-rhio   and JDBC Additional Functionality Currently at least two general prototypes have been envisioned with the following functionality Being able to represent average values for root parameters, such as length, and display them in a readable and understandable manner Being able to represent the lifecycle of a root based on the parameters recorded on the first day of growth to the last. It is likely that each of these general prototypes will have sub-prototypes that will evaluate techniques that display the information to achieve the objective. After the more successful techniques have been identified they will be developed and presented to the research group to confirm if they still wish this prototype to be included. Design Methodology To ensure the success of this project, the implementation of this project will follow an iterative software development methodology of design, implementation and testing.   This has been selected because one of the key components of this project is to build upon a basic system. It is therefore essential that this be successfully deployed before introducing any further functionality from desired prototypes. Due to this requirement the selected methodology will be The Spiral, which supports iterations and constant reviews and refinements as shown in Figure 3. It is also vital to review each prototype after it has been implemented to ensure that the original functionality has not been compromised. Figure 3 – Spiral Software Development Methodology taken from http://as.exeter.ac.uk/divisions/exeterit/iws/projects/ede/ Risk Analysis As with any software development project there is a certain element of risk in undertaking the assignment, and the key risks identified within this project are as follows: Terminology Due to biological terminology involved in the understanding of the system requirements there is a possibility that these may be interpreted wrongly. Therefore it is imperative that this be considered at every stage involving an element of design, and clarification will be sought if any ambiguity arises. Time Constraints As this project is due to be completed over a 15-week period there is potential for the project to overrun at any point and so continual review of the work plan is required. The stage with the highest potential for delay would be that of developing and testing prototypes.   Once the initial prototypes have been produced there is a possibility that revision will be required after discussions with Dr Amtmann’s Research Team.   Where possible all prototypes will be implemented to the highest standard however at a given point the further development of the prototypes will have to be discontinued. Regular discussions with Dr Rogers and Dr Amtmann regarding this should identify a suitable stopping point and prevent the project from suffering from delays. 5. Work Plan This chapter will detail the provisional work plan for this project and it has been estimated that work will begin on 20th June 2011.   Please note that weekly meetings will be scheduled with Dr Simon Rogers during this time and there will be a high level of communication between Dr Anna Amtmann’s Research Group and myself. Figure 4 – Gantt Chart Showing Estimated Project Timescale The following sections detail the work that will be done in each phase of the project as shown in the Gantt chart in Figure 4. Background Research During this time I will study the areas identified in section three in more depth and review this background research.   At this stage I will also investigate further techniques that could be used to develop additional prototypes that could be used to manipulate and display the root data in an interesting manner. Deliverables: Revised Background Section Critical Rating: Preferably Risk Factor: Low Requirements Capture This stage will involve identifying and creating a detailed list of the functional and non-functional requirements of the system.   During this time a requirements document containing the appropriate UML diagrams will be developed and will be approved by Dr Simon Rogers and Dr Anna Amtmann before any implementation begins. Deliverables: A clear requirements document Critical Rating: Important Risk Factor: Medium Implementation of the Basic Functionality After the basic requirements of the system have been determined they will be implemented in an application. At this stage, the system should at a minimum be able to use the data from EZ-Rhizo and create a visualization of at least one root. A test plan will also be created at this time. Deliverables: Implementation of Basic System and Test Plan Critical Rating: Fundamental Risk Factor:   Medium Testing of Basic Functionality This stage will ensure that the basic system is operational and that all of the functional requirements have been achieved.   This may involve getting feedback from the Plant Science Group depending on their availability and whether or not they wish to test the completed project. Deliverables: Critical Rating: Important Risk Factor: Low Design and Build Prototypes The next section will involve developing prototypes that manipulate and display the root data in various interesting manners.   Each prototype will involve a technique identified previously in section three or found within the further research conducted at the beginning of the project plan. Deliverables: A number of prototypes that modify the visualization of a basic root Critical Rating: Fundamental Risk Factor: Low Experiments with Prototypes At this time Dr Amtmann and her team will review the prototypes with a follow up discussion with Dr Rogers and myself to confirm that they capture useful information.   At this time, depending on time constraints, additional prototypes may be developed based on the discussions, however this will be reviewed at the time. Deliverables: A list of prototypes that are deemed to be visually interesting and useful Critical Rating: Preferable Risk Factor: Medium Integrate desired Prototypes Any prototypes that are deemed to be successful will then be integrated into the basic system.   This will be completed in iterations, with testing being conducted after each integration to ensure that the new prototype does not break the basic system. Deliverables: Critical Rating: Important Risk Factor: High Testing of Additional Functionality Once every prototype has been implemented successfully, further testing will be conducted to ensure that the original functionality still meets the initial criteria and that the prototype functionality is producing valid and interesting results.   At this stage a small user evaluation may be conducted to ensure that the user interface developed for the system is successful. Deliverables: Critical Rating: Important Risk Factor: Low Complete and Submit Dissertation Finally, a dissertation documenting the work that has been undertaken will be composed and will be approximately 60-70 pages long.   It will be submitted on the 6th September 2011.   During the project, work will be provisionally documented to ensure an easier transition from implementation to documentation. Deliverables: A complete dissertation documenting the progress and outcome of this project Critical Rating: Fundamental Risk Factor: Low Bibliography (Armengaud et al, 2009) Armengaud, P., Zambaux, K., Hills, A., Sulpice, R., Pattison, R. J., Blatt, M. R. and Amtmann, A. (2009), EZ-Rhizo: integrated software for the fast and accurate measurement of root system architecture. The Plant Journal, 57:  pp. 945–956. DOI:  10.1111/j.1365-313X.2008.03739.x (Den Herder et al, 2010) Den Herder, G., Van Isterdael, G., Beeckman, T. and De Smet, I. (2010), The roots of a new green revolution, Trends in Plant Science, Volume 15, Issue 11, November 2010, pp. 600-607. DOI: 10.1016/j.tplants.2010.08.009. (Godfray et al, 2010) Godfray, H.C., Beddington, J.R., Crute, I.R., Haddad, L., Lawrence, D, Muir, J.F., Pretty, J. Thomas, S.M. and Toulmin, C. (2010), The challenge of feeding 9 billion people. Science Magazine, 12th February 2010: 327 (5967), pp. 812-818. DOI: 10.1126/science.1185383 (Malamy, 2005) Malamy, J. E. (2005), Intrinsic and environmental response pathways that regulate root system architecture. Plant, Cell Environment, 28:  pp. 67–77. DOI:  10.1111/j.1365-3040.2005.01306.x (PRB, 2004) Population Reference Bureau, www.prb.org/pdf04/04worlddatasheet_eng.pdf, accessed 20th March 2011 (Population Reference Bureau, 2010) Population Reference Bureau, www.prb.org/Publications/Datasheets/2010/2010wpds.aspx, accessed 20th March 2011 (Somerville and Koorneef, 2002) Somerville, C. and Koornneef, M. (2002), A fortunate choice: the history of Arabidopsis as a model plant. Nature Reviews Genetics, 3:11, pp. 883 889 http://dx.doi.org/10.1038/nrg927 (United Nations, 2004) United Nations Department of Economic and Social Affairs: Population Division, un.org/esa/population/publications/longrange2/WorldPop2300final.pdf, accessed 2nd March 2011 (United Nations, 2010) Food and Agriculture Organization of United Nations, www.fao.org/hunger/en/, accessed 2nd March 2011 (Wikipedia, 2011) Wikipedia. Root Wikipedia, the free encyclopaedia. Retrieved March 2011 from http://en.wikipedia.org/wiki/Root (Wikipedia, 2011) Wikipedia. Genome Wikipedia, the free encyclopaedia. Retrieved March 2011 from http://en.wikipedia.org/wiki/Genome_sequence

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