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User talk:Mariogil

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title: User talk:Mariogil


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title: File:Egm6341.s10.mtg40.djvu


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User talk:Shinedog~enwikiversity

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title: User talk:Shinedog~enwikiversity


text:
Curriculum on Digital Libraries/Routing

 * Older versions of the draft developed by UNC/VT Project Team (2009-10-07 PDFWORD)

Module name
Routing

Scope
This module presents an overview of models and practices of Routing, leading to better search result quality in Digital Libraries.

Learning objectives:
By the end of this lesson, the student will be able to:
 * a. Explain the need for routing in digital libraries
 * b. Explain the basic concepts of routing
 * c. Identify the different types and methodologies of routing systems
 * d. Explain the challenges involved in routing techniques

5S characteristics of the module:

 * a. Stream: Routing systems primarily deal with textual information. But they also may handle media types such as images, audio, and video.
 * b. Structure: Routing requires particular structures for queries, profiles and forward knowledge systems.
 * c. Space: In routing, vector space finds the similarity of query and document vectors.
 * d. Scenario: The process of routing the query to the identified appropriate digital reference services.
 * e. Society: End users (such as librarians, computer programmers, journalists) requiring information and systems (such as routers) that facilitate the retrieval.

Level of effort required:

 * a. In class: 4 hours
 * b. Outside of class: 4 hours
 * i. Reading before the class starts: 2 hours
 * ii. Homework assignment: 1 hour (refer to Exercises section)

Relationships with other modules:

 * 6-a:Digital_Libraries/Online_info_seeking_behavior_and_search_strategy
 * 7-b:Digital_Libraries/Reference_services

Prerequisite knowledge required:

 * a. General understanding of library reference services
 * b. A prior basic understand of internet infrastructure

Introductory remedial instruction:

 * a. None

I.	Need for Routing
With the rapidly growing, heterogeneous, distributed collection of data sources available in the Internet, locating and accessing information has been a significant problem. This is mainly applicable for large-scale digital libraries. This problem is made worse by the rapid growth in information in the web and by the increasing number of naive users who typically issue very short or broadly defined queries to search systems. Query routing techniques offer an important advantage of facilitating focused resource discovery. This enables the queries to focus on certain semantic characteristics of the resource and the discovery focuses on a specific category of resources.

II.	Introduction to Routing
Query routing is used to redirect the queries from the user to the appropriate digital reference services. The main idea of query routing is to restrict the scope of the search through various methods, thus making it effective. An efficient query routing system minimizes the response time for fetching the results by reducing unnecessary communication overhead over the network to the individual digital reference services. So, the main goal of query routing is to route a user query to the most relevant information sources that can provide the best answer.

III.	Types of Routing Systems:

 * a. Query Routing
 * i. Manual query routing services
 * General-purpose search engines (such as AltaVista, Lycos) are known for returning non-relevant search results for the user query. This led to the demand for topic specific search. Some search engines (such as VacationSpot.com, KidsHealth.org etc.) provide a categorized list of specialized search engines for particular topics. Conventional query routing services to specific search engines are done manually by the user. The users themselves are required to choose appropriate search engines from the list based on the information need.

 * ii. Automated query routing systems
 * There are certain routing systems which perform automated query routing. Some of these systems use content summaries for categorizing the databases based on the list of term-frequency pairs. With these summaries, the systems can identify if a particular database is relevant to the user query or not.

 * b. Document Routing
 * In document routing, the file or the document is routed from one user to another in a network. This type of routing is quite similar to query routing except that the content which gets transferred is a document in this case. The methods and algorithms used to route the content will mostly be the same in all the cases. This type of routing usually happens in a peer-to-peer file sharing network, where one peer tries to share a file to another node across the network.

 * c. Selective Dissemination of Information (SDI) Systems:
 * With the wealth of information available in the internet, the volume of information available ranging across all interests has also increased. As already explained in the need for query routing, it is difficult to query and filter formation pertaining to individual interests. The aim of an SDI system is to deliver new information arriving at an SDI-aware information provider to users who express their interests through user profiles.

 * There are various ways of implementing SDI systems - one such option being persistent query mechanisms, where the users create a query and add them to the system. As the queries remain resident in the system, successful matches with incoming documents to the information sources are identified and routed to the particular user.

 * {| class="wikitable" style="width:300px border="1"

! Content Routed!! Routing Type
 * style="width:150px" align=center | user query || style="width:150px" align=center | Query Routing
 * style="width:150px" align=center | file / document || style="width:150px" align=center | Document Routing
 * style="width:150px" align=center | information of user's interest|| style="width:150px" align=center | Selective Dissemination of Information (SDI)
 * }
 * style="width:150px" align=center | information of user's interest|| style="width:150px" align=center | Selective Dissemination of Information (SDI)
 * }
 * }

IV.	Query routing architecture

 * a. Mediator based routers:
 * A query routing system could have any structure depending on network topology. One such system looks like a directed acyclic graph with router nodes present in the middle and the information providers are the leaf nodes. This hierarchy is shown in figure-1. The router nodes usually contain the content summary and query capability description of the information providers that are registered with it. In some methods, the role of query routers is done by the mediator, which contains forward knowledge about the digital reference services.

 * [[Image:fig1-routing.png|600px]]

 * b. Peer to Peer based routers:
 * In the case of peer to peer methods, the routing is done by each peer in the network. One good example is the Gnutella network which is shown in figure-2.

 * [[Image:fig2-routing.png|500px]]

V.	Query routing methods

 * a. Forward knowledge systems
 * Effective query routing has to reduce both false positive and false negative results. In the context of information retrieval, false positive results are the non-relevant search results that are returned to the user. Such results will not help in fulfilling the user’s information need. False negatives are those results that are very much relevant to the user’s query but fail to be fetched by the system. Identifying false positives and false negatives an be done as a two fold process – one focusing on reducing false positive and another on minimizing false negative results.

 * Query Refinement
 * This is the process of specifying and modifying the query definition to focus on the information need. In some cases, the user is overwhelmed with unwanted information which is non-relevant to the query. This is possible because of broadly defined queries from a naïve user. Query refinement aims at refining and suggesting queries to the user in order to focus it only on the documents of interest. There are some approaches in refining the user queries:

 * i. Collocation of terms
 * This is the common way of refining the queries. This approach is a two-fold process where the set of documents containing terms from a user’s query are identified and then query suggestions are made with highest cumulative frequency of these documents. For example, For instance, in response to a query ``book suppliers", the query routing system will derive the following recommended terms: ``book store", ``book club", ``publisher".

 * ii. User Profiles
 * Another query refinement approach is to maintain profiles for the user using which the suggestions are derived for well focused queries. The main goal of this approach is to suggest replacement terms based on the semantic context and the scope of what the user requires from a particular query. These suggested terms are derived from domain-specific ontology or the user's feedback on the query context. Hence the recommendation is primarily based on the domain knowledge of the keywords present in the original user query. This approach reduces false positives and hence improves the efficiency and accuracy of the query refinement process.

 * The construction of the user query profiles can be done by using some domain-specific ontology or use an interactive dialog where the user can enter the synonyms of the query. This shows that the user profiles are not dependent on the information providers. So, even if the number of digital reference services increase, the answers are fetched seamlessly, thus enabling scalability.

 * Source Selection
 * This helps the users to identify and locate the correct digital reference service for a particular query thereby reducing the false negatives in the result. This mechanism aims at minimizing the overhead of reaching those digital reference services that do not satisfy the user query by pruning the non-relevant ones. Usually the user is interested in searching all the digital reference services that serve to satisfy the results.

 * For a given query, there can be many ways of searching for a particular information source. One way is to fetch all documents matching the query while other is to fetch those data sources which have high relevance – more matching documents than others. Given a query and search semantics, there are a number of approaches to find the best data sources for a particular query. This depends on the types and capabilities of the information sources. This is in alternate to searching all available information sources that contain matching documents.

 * i. Content summary:
 * This approach is popular in mediator based systems. Each router contains the summary of the terms which are handled by the data source associated with it. This enables the router to check for the relevance of the query with the content summary and thus redirecting it.

 * ii. Query Capability:
 * The query capabilities contain the capability descriptions of the source which are used to evaluate the relevance of a particular query. The data sources which contain the capability to handle the query and returning non-empty results are then chosen as relevant information sources for executing the query.

 * There are two main drawbacks in using both content summary and query capability. Systems using these methods require manual identification and collection of information on content summary and query capability. This is not feasible for large systems involving huge amount of information in a particular data source. The present systems which support capability based support do not perform query refinement which makes it ineffective to yield best routing results.

 * iii. Source Quality Measurements:
 * In order to select a particular information source, its quality measurements can be used. The quality dimensions include accuracy, reputation, timeliness, completeness, understandability of the sources. One disadvantage of this approach is its subjective nature. The quality of source selection will drop dramatically when the query users do not share the given set of information quality measurements.

 * The overall process of forward knowledge based systems and its work flow is shown in figure-3.

 * [[Image:fig3-routing.png|600px]]

 * b. Peer-to-Peer based routing
 * Peer to peer systems are the most popular medium through which huge amounts of data are shared. Their ability to build a resource-rich system by aggregating resources gives them more advantage than the centralized systems. The query forwarding problem of the distributed search system and the scalability limitations of the centralized search engines are overcome by the peer based web search. Routing on these networks is either centralized or statically configured and is therefore unproblematic.

 * In the peer-based web search, each peer uses the results of its interaction with its neighbors to learn and rank them every time a query is encountered. This ranking is based on how well the other node in the network has returned the search results for a particular query. This model is used to dynamically route the queries according to the predicted match with other peer’s knowledge. Also, the network topology is modified on the fly based on learned contexts and current information needs.

 * Overlay networks are a class of peer to peer networks where a virtual topology is built on top of physical links of the network. The uptime (active time) of a peer is relatively low. So, they leave and join the network whenever needed. This makes the topology of the network more dynamic. Routing in such systems is really problematic since a particular route will be valid only for a short period of time before a node in that route leaves the network.

 * Challenges in peer-to-peer routing algorithms:
 * Scalability: This is a measure of how a system performs when the number of nodes and hence the number of communications on the network grows.

 * Complexity: This is the measure of the number of intermediate steps involved before a packet travels from one peer node to another in a worst case scenario.

 * Anonymity: The anonymity factor is not applicable for all the peer based systems. There are certain systems which require the feature of anonymity. Handling the anonymity of peers should be done at the routing level.

 * Example Peer based models:
 * i. Gnutella Routing algorithm:
 * Gnutella is a file sharing network which uses overlay network model. The basic idea is that each node maintains a connection to a number of other nodes. In order to search any information in a network, the client broadcasts the message to all its peers. The node receiving the query, forwards it to its peers. This goes on until the required resource is found. The found resource is then returned back along the path sent. Since this process is recursive, the number of nodes queried has to be controlled. This is done using Time-to-Live counter. This implies the number of nodes or the time within which the resource has to be found. If the resource is not found within this limit, then a failure message is sent to client (where the query originated). This type of routing is the simplest kind possible for an overlay network. The network topology of Gnutella algorithm can be seen in figure-2.

 * This algorithm and network routing has many problems. The flooding of associated peers with query will work well for small or medium sized networks. But the cost of searching on this type of peer-to-peer network is more and as the network scales large, the cost increases exponentially. Gnutella was not designed to be anonymous and discovering who is making specific resources available is a simple matter of performing a search.

 * ii. Distributed Hash Tables:
 * Distributed Hash Table algorithms are decentralized distributed systems that use hash function for sharing key across peers. Any participating node can efficiently retrieve the value associated with a given key. DHT algorithms are useful for sharing files or other data across a peer-to-peer network. The hash function used here takes a string of variable length and returns a number that it generates. This algorithm works by hashing all data identifiers and storing their locations in a hash table. This table is distributed across all participating nodes.

 * Many DHTs use the flexibility to pick neighbors which are close in terms of latency in the physical underlying network. So, the maximum route length is closely related to the diameter, which is the maximum number of hops in any shortest path between nodes. Similar to graph theory, this algorithm is limited by the degree/diameter tradeoff. One main advantage of this algorithm is its time complexity of O (log n). It has lack of significant overhead which makes it better than the flooding algorithm. One good example of a DHT algorithm is chord, which is developed by a research group in MIT.

 * iii. Key-based routing (KBR)
 * Key-based routing is a lookup method which is used with overlay networks such as DHTs. DHTs provide a method to identify the host where the data is present. Key-based routing provides a method to find the closest host for the data which is present in host. The host is said to be closest by the number of network hops rather than the physical distance.

 * Freenet is one example of a distributed peer to peer network which is based on key-based routing. In this case, each file or data is associated with a key and files with similar keys are likely to cluster on similar set of nodes. The clusters are identified and the query is routed to them without needing to search many peers, thus minimizing the search space. This algorithm provides anonymity and efficient routing of information.

 * Each node on the network contains data which is not known by its peers. So, each node acts as a data store to which other nodes can read or write. This enables the network to act as a distributed file-system. The data on each node is encrypted and is not known to other nodes providing anonymity.

 * Each node has a routing table which has the address of its neighbors and their performance in returning particular keys. The performance is assessed by its response time and transfer time. When a node receives a request, it searches its routing table to find the node which has been most successful in returning a similar key before. It is the keys which are used for finding relevance and not the files.

 * There are many advantages in this type of routing, such as anonymity, improved efficiency over time etc. But the drawback is that the network is slow as the data must pass through all the intermediate nodes.

 * The query routing flow in a Freenet algorithm is discussed in figure-4.

 * [[Image:fig4-routing.png|500px]]

 * iv. Semantic Routing
 * Semantic routing is a method which is more focused on the nature of query to be routed and it does not count on the network topology. This algorithm prioritizes the nodes which have been previously good at providing information about the types of content referred to by the query.

 * In order to search for information in a peer to peer network using semantic routing, the data needs to have some semantic description associated with it. This usually is the metadata information for the data. Tagging the data improves the semantic nature in a peer-to-peer network.

 * This algorithm modifies the confidence on a particular peer if it answers a query. If the node has answered the query correctly, then the confidence measure is increased. There must be a constant identifier in the network’s namespace to retain the confidence ratings. This is done in order to achieve persistence.

For Students

 * Le-Shin Wu, Ruj Akavipat, Filippo Menczer. 2005. Adaptive Query Routing in Peer Web Search. ACM, 1074-1075. DOI= http://doi.acm.org/10.1145/1062745.1062875.

 * Arturo Crespo and Hector Garcia-Molina. 2002. Routing Indices For Peer-to-Peer Systems. Proceedings of the 22 nd International Conference on Distributed Computing Systems (ICDCS'02).  IEEE Press. 23. DOI=http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.123.8939

 * Miguel Castro Peter, Peter Druschel,  Ayalvadi Ganesh,  Antony Rowstron,  Dan S. Wallach. 2002. Secure routing for structured peer-to-peer overlay networks. 299-314. ACM. DOI=http://doi.acm.org/10.1145/1060289.1060317

 * Ling Liu. 1999. Query Routing in Large-scale Digital Library Systems. Computer Society. International Conference on Data Engineering (ICDE'99). IEEE Press. 154. DOI=http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.10.6346

 * Wai Gen Yee, Linh Thai Nguyen, Dongmei Jia, Ophir Frieder. 2008. Efficient query routing by improved peer description in P2P networks. International conference on Scalable information systems. (ICST’08). DOI=http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.143.4994

For Faculty

 * A. Sugiura and O. Etzioni. 2000. Query Routing for Web Search Engines: Architecture and Experiments. North-Holland Publishing Co. 417 – 429. DOI = http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.79.522

 * Anjali Gupta, Barbara Liskov, Rodrigo Rodrigues. 2004. Efficient Routing for Peer-to-Peer Overlays. Proceedings of the 1st conference on Symposium on Networked Systems Design and Implementation - Volume 1. USENIX Association. 9. DOI = http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1.5939

 * Gibbins, N. and Hall, W. 2001. Scalability Issues for Query Routing Service Discovery. Proceedings of the Second Workshop on Infrastructure for Agents, MAS and Scalable MAS. 209-217. DOI= http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.28.7362

 * Abhishek Kumar, Jun (Jim) Xu, Ellen W. Zegura. 2005. Efficient and scalable query routing for unstructured peer-to-peer networks. 24th Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings IEEE. 1162- 1173. DOI= 10.1109/INFCOM.2005.1498343

Exercises / Learning activities
a) Class Activity - Adaptive Query Routing demonstration (20 minutes)

Visit the link below to view the online demonstration on Adaptive Query Routing: http://www.cc.gatech.edu/projects/disl/QR/

This is the homepage of the query routing software. AQR is an adaptive middleware layer that uses several query routing mechanisms and provides architecture for deploying them in an open networked environment. This online routing system was developed by Associate Professors Ling Liu and Carlton Pu with the help of graduate students David Buttler, Wei Han, Henrique Paques, and Wei Tang.

Select the ‘walkthrough’ section on the left panel. It navigates the user to the demo of query routing system. It provides an interactive demo of query routing aiding to understand the basic concepts and process involved in it. Break the class into 3-4 groups.

 * i) See the step-by-step process of query routing shown in walkthrough. Each group has to analyze and relate it to the techniques of query routing discussed in the syllabus 9.V. (Hint: This system uses the process of query refinement and source selection.)

 * ii) Each group prepares a report on what happens in each stage of the routing process.

b) In-class Exercise - Analysis of Freenet (20 minutes)

Visit http://freenetproject.org/ which contains the Freenet application. This is based on peer to peer routing model.

Read the routing technique used in Freenet. Download the software and try it (This is optional because the software might consume a lot of disk space).

 * i) Identify the need for using routing in this application.
 * ii) What type of routing technique does this system employ?
 * iii) What is the purpose of using key-based algorithm in this application?
 * iv) How is anonymity achieved in this peer to peer model?
 * v) Identify the advantages and drawbacks of this model.

Prepare a report on the above questions.

More details on Freenet routing can be found in http://freenetproject.org/ngrouting.html

c) Class Activity – Compare and Contrast (15 minutes)

Compare and contrast the approaches used in 12 (a) and 12 (b). 12 (a) is a class activity on Adaptive Query Routing demonstration and 12 (b) is an exercise about Freenet. Both these systems follow different methodologies. In class, break the students into groups of 3 or 4. Have them discuss the differences in the methodologies used by AQR and Freenet. Identify the advantages and disadvantages of the methods. Let each group prepare a Word document or PowerPoint slides (5 minutes) and presents it to the class (10 minutes).

Evaluation of learning outcomes
In their answers to the discussion questions, the students demonstrate an understanding of:
 * The need for routing systems
 * The basic concepts of routing
 * Different types and methodologies of routing techniques
 * Challenges involved in routing queries

Glossary
DRS: Digital Reference Service

Clustering: grouping of documents which satisfy a set of common properties. The aim is to assemble together documents which are related among them. Clustering can be used, for instance, to expand a user query with new and related index terms.

Scalability: The ability and ease with which a service may increase in size; where size may be defined as number of users, throughput of questions and responses, etc.

Vector model: an algebraic model for representing text documents (and any objects, in general) as vectors of identifiers. It is used in information filtering, information retrieval, indexing and relevancy rankings.

Additional useful links

 * Adaptive Query Routing at http://www.cc.gatech.edu/projects/disl/QR/

 * Freenet at http://freenetproject.org/

 * Chord at http://pdos.csail.mit.edu/chord/

 * Peer to peer routing at http://ntrg.cs.tcd.ie/undergrad/4ba2.05/group6/index.html

Contributors
Authors:
 * Venkatasubramaniam Ganesan
 * Dr. Edward Fox

Evaluators:
 * Ashwin Palani
 * Ashwin Khandeparker
 * Seungwon Yang

title: Curriculum on Digital Libraries/Routing


title: File:Fig1-routing.png


title: File:Fig2-routing.png


title: File:Fig3-routing.png


title: File:Fig4-routing.png


text:
Digital Libraries/Routing

 * Older versions of the draft developed by UNC/VT Project Team (2009-10-07 PDFWORD)

Module name
Routing

Scope
This module presents an overview of models and practices of Routing, leading to better search result quality in Digital Libraries.

Learning objectives:
By the end of this lesson, the student will be able to:
 * a. Explain the need for routing in digital libraries
 * b. Explain the basic concepts of routing
 * c. Identify the different types and methodologies of routing systems
 * d. Explain the challenges involved in routing techniques

5S characteristics of the module:

 * a. Stream: Routing systems primarily deal with textual information. But they also may handle media types such as images, audio, and video.
 * b. Structure: Routing requires particular structures for queries, profiles and forward knowledge systems.
 * c. Space: In routing, vector space finds the similarity of query and document vectors.
 * d. Scenario: The process of routing the query to the identified appropriate digital reference services.
 * e. Society: End users (such as librarians, computer programmers, journalists) requiring information and systems (such as routers) that facilitate the retrieval.

Level of effort required:

 * a. In class: 4 hours
 * b. Outside of class: 4 hours
 * i. Reading before the class starts: 2 hours
 * ii. Homework assignment: 1 hour (refer to Exercises section)

Relationships with other modules:

 * 6-a:Digital_Libraries/Online_info_seeking_behavior_and_search_strategy
 * 7-b:Digital_Libraries/Reference_services

Prerequisite knowledge required:

 * a. General understanding of library reference services
 * b. A prior basic understand of internet infrastructure

Introductory remedial instruction:

 * a. None

I.	Need for Routing
With the rapidly growing, heterogeneous, distributed collection of data sources available in the Internet, locating and accessing information has been a significant problem. This is mainly applicable for large-scale digital libraries. This problem is made worse by the rapid growth in information in the web and by the increasing number of naive users who typically issue very short or broadly defined queries to search systems. Query routing techniques offer an important advantage of facilitating focused resource discovery. This enables the queries to focus on certain semantic characteristics of the resource and the discovery focuses on a specific category of resources.

II.	Introduction to Routing
Query routing is used to redirect the queries from the user to the appropriate digital reference services. The main idea of query routing is to restrict the scope of the search through various methods, thus making it effective. An efficient query routing system minimizes the response time for fetching the results by reducing unnecessary communication overhead over the network to the individual digital reference services. So, the main goal of query routing is to route a user query to the most relevant information sources that can provide the best answer.

III.	Types of Routing Systems:

 * a. Query Routing
 * i. Manual query routing services
 * General-purpose search engines (such as AltaVista, Lycos) are known for returning non-relevant search results for the user query. This led to the demand for topic specific search. Some search engines (such as VacationSpot.com, KidsHealth.org etc.) provide a categorized list of specialized search engines for particular topics. Conventional query routing services to specific search engines are done manually by the user. The users themselves are required to choose appropriate search engines from the list based on the information need.

 * ii. Automated query routing systems
 * There are certain routing systems which perform automated query routing. Some of these systems use content summaries for categorizing the databases based on the list of term-frequency pairs. With these summaries, the systems can identify if a particular database is relevant to the user query or not.

 * b. Document Routing
 * In document routing, the file or the document is routed from one user to another in a network. This type of routing is quite similar to query routing except that the content which gets transferred is a document in this case. The methods and algorithms used to route the content will mostly be the same in all the cases. This type of routing usually happens in a peer-to-peer file sharing network, where one peer tries to share a file to another node across the network.

 * c. Selective Dissemination of Information (SDI) Systems:
 * With the wealth of information available in the internet, the volume of information available ranging across all interests has also increased. As already explained in the need for query routing, it is difficult to query and filter formation pertaining to individual interests. The aim of an SDI system is to deliver new information arriving at an SDI-aware information provider to users who express their interests through user profiles.

 * There are various ways of implementing SDI systems - one such option being persistent query mechanisms, where the users create a query and add them to the system. As the queries remain resident in the system, successful matches with incoming documents to the information sources are identified and routed to the particular user.

 * {| class="wikitable" style="width:300px border="1"

! Content Routed!! Routing Type
 * style="width:150px" align=center | user query || style="width:150px" align=center | Query Routing
 * style="width:150px" align=center | file / document || style="width:150px" align=center | Document Routing
 * style="width:150px" align=center | information of user's interest|| style="width:150px" align=center | Selective Dissemination of Information (SDI)
 * }
 * style="width:150px" align=center | information of user's interest|| style="width:150px" align=center | Selective Dissemination of Information (SDI)
 * }
 * }

IV.	Query routing architecture

 * a. Mediator based routers:
 * A query routing system could have any structure depending on network topology. One such system looks like a directed acyclic graph with router nodes present in the middle and the information providers are the leaf nodes. This hierarchy is shown in figure-1. The router nodes usually contain the content summary and query capability description of the information providers that are registered with it. In some methods, the role of query routers is done by the mediator, which contains forward knowledge about the digital reference services.

 * [[Image:fig1-routing.png|600px]]

 * b. Peer to Peer based routers:
 * In the case of peer to peer methods, the routing is done by each peer in the network. One good example is the Gnutella network which is shown in figure-2.

 * [[Image:fig2-routing.png|500px]]

V.	Query routing methods

 * a. Forward knowledge systems
 * Effective query routing has to reduce both false positive and false negative results. In the context of information retrieval, false positive results are the non-relevant search results that are returned to the user. Such results will not help in fulfilling the user’s information need. False negatives are those results that are very much relevant to the user’s query but fail to be fetched by the system. Identifying false positives and false negatives an be done as a two fold process – one focusing on reducing false positive and another on minimizing false negative results.

 * Query Refinement
 * This is the process of specifying and modifying the query definition to focus on the information need. In some cases, the user is overwhelmed with unwanted information which is non-relevant to the query. This is possible because of broadly defined queries from a naïve user. Query refinement aims at refining and suggesting queries to the user in order to focus it only on the documents of interest. There are some approaches in refining the user queries:

 * i. Collocation of terms
 * This is the common way of refining the queries. This approach is a two-fold process where the set of documents containing terms from a user’s query are identified and then query suggestions are made with highest cumulative frequency of these documents. For example, For instance, in response to a query ``book suppliers", the query routing system will derive the following recommended terms: ``book store", ``book club", ``publisher".

 * ii. User Profiles
 * Another query refinement approach is to maintain profiles for the user using which the suggestions are derived for well focused queries. The main goal of this approach is to suggest replacement terms based on the semantic context and the scope of what the user requires from a particular query. These suggested terms are derived from domain-specific ontology or the user's feedback on the query context. Hence the recommendation is primarily based on the domain knowledge of the keywords present in the original user query. This approach reduces false positives and hence improves the efficiency and accuracy of the query refinement process.

 * The construction of the user query profiles can be done by using some domain-specific ontology or use an interactive dialog where the user can enter the synonyms of the query. This shows that the user profiles are not dependent on the information providers. So, even if the number of digital reference services increase, the answers are fetched seamlessly, thus enabling scalability.

 * Source Selection
 * This helps the users to identify and locate the correct digital reference service for a particular query thereby reducing the false negatives in the result. This mechanism aims at minimizing the overhead of reaching those digital reference services that do not satisfy the user query by pruning the non-relevant ones. Usually the user is interested in searching all the digital reference services that serve to satisfy the results.

 * For a given query, there can be many ways of searching for a particular information source. One way is to fetch all documents matching the query while other is to fetch those data sources which have high relevance – more matching documents than others. Given a query and search semantics, there are a number of approaches to find the best data sources for a particular query. This depends on the types and capabilities of the information sources. This is in alternate to searching all available information sources that contain matching documents.

 * i. Content summary:
 * This approach is popular in mediator based systems. Each router contains the summary of the terms which are handled by the data source associated with it. This enables the router to check for the relevance of the query with the content summary and thus redirecting it.

 * ii. Query Capability:
 * The query capabilities contain the capability descriptions of the source which are used to evaluate the relevance of a particular query. The data sources which contain the capability to handle the query and returning non-empty results are then chosen as relevant information sources for executing the query.

 * There are two main drawbacks in using both content summary and query capability. Systems using these methods require manual identification and collection of information on content summary and query capability. This is not feasible for large systems involving huge amount of information in a particular data source. The present systems which support capability based support do not perform query refinement which makes it ineffective to yield best routing results.

 * iii. Source Quality Measurements:
 * In order to select a particular information source, its quality measurements can be used. The quality dimensions include accuracy, reputation, timeliness, completeness, understandability of the sources. One disadvantage of this approach is its subjective nature. The quality of source selection will drop dramatically when the query users do not share the given set of information quality measurements.

 * The overall process of forward knowledge based systems and its work flow is shown in figure-3.

 * [[Image:fig3-routing.png|600px]]

 * b. Peer-to-Peer based routing
 * Peer to peer systems are the most popular medium through which huge amounts of data are shared. Their ability to build a resource-rich system by aggregating resources gives them more advantage than the centralized systems. The query forwarding problem of the distributed search system and the scalability limitations of the centralized search engines are overcome by the peer based web search. Routing on these networks is either centralized or statically configured and is therefore unproblematic.

 * In the peer-based web search, each peer uses the results of its interaction with its neighbors to learn and rank them every time a query is encountered. This ranking is based on how well the other node in the network has returned the search results for a particular query. This model is used to dynamically route the queries according to the predicted match with other peer’s knowledge. Also, the network topology is modified on the fly based on learned contexts and current information needs.

 * Overlay networks are a class of peer to peer networks where a virtual topology is built on top of physical links of the network. The uptime (active time) of a peer is relatively low. So, they leave and join the network whenever needed. This makes the topology of the network more dynamic. Routing in such systems is really problematic since a particular route will be valid only for a short period of time before a node in that route leaves the network.

 * Challenges in peer-to-peer routing algorithms:
 * Scalability: This is a measure of how a system performs when the number of nodes and hence the number of communications on the network grows.

 * Complexity: This is the measure of the number of intermediate steps involved before a packet travels from one peer node to another in a worst case scenario.

 * Anonymity: The anonymity factor is not applicable for all the peer based systems. There are certain systems which require the feature of anonymity. Handling the anonymity of peers should be done at the routing level.

 * Example Peer based models:
 * i. Gnutella Routing algorithm:
 * Gnutella is a file sharing network which uses overlay network model. The basic idea is that each node maintains a connection to a number of other nodes. In order to search any information in a network, the client broadcasts the message to all its peers. The node receiving the query, forwards it to its peers. This goes on until the required resource is found. The found resource is then returned back along the path sent. Since this process is recursive, the number of nodes queried has to be controlled. This is done using Time-to-Live counter. This implies the number of nodes or the time within which the resource has to be found. If the resource is not found within this limit, then a failure message is sent to client (where the query originated). This type of routing is the simplest kind possible for an overlay network. The network topology of Gnutella algorithm can be seen in figure-2.

 * This algorithm and network routing has many problems. The flooding of associated peers with query will work well for small or medium sized networks. But the cost of searching on this type of peer-to-peer network is more and as the network scales large, the cost increases exponentially. Gnutella was not designed to be anonymous and discovering who is making specific resources available is a simple matter of performing a search.

 * ii. Distributed Hash Tables:
 * Distributed Hash Table algorithms are decentralized distributed systems that use hash function for sharing key across peers. Any participating node can efficiently retrieve the value associated with a given key. DHT algorithms are useful for sharing files or other data across a peer-to-peer network. The hash function used here takes a string of variable length and returns a number that it generates. This algorithm works by hashing all data identifiers and storing their locations in a hash table. This table is distributed across all participating nodes.

 * Many DHTs use the flexibility to pick neighbors which are close in terms of latency in the physical underlying network. So, the maximum route length is closely related to the diameter, which is the maximum number of hops in any shortest path between nodes. Similar to graph theory, this algorithm is limited by the degree/diameter tradeoff. One main advantage of this algorithm is its time complexity of O (log n). It has lack of significant overhead which makes it better than the flooding algorithm. One good example of a DHT algorithm is chord, which is developed by a research group in MIT.

 * iii. Key-based routing (KBR)
 * Key-based routing is a lookup method which is used with overlay networks such as DHTs. DHTs provide a method to identify the host where the data is present. Key-based routing provides a method to find the closest host for the data which is present in host. The host is said to be closest by the number of network hops rather than the physical distance.

 * Freenet is one example of a distributed peer to peer network which is based on key-based routing. In this case, each file or data is associated with a key and files with similar keys are likely to cluster on similar set of nodes. The clusters are identified and the query is routed to them without needing to search many peers, thus minimizing the search space. This algorithm provides anonymity and efficient routing of information.

 * Each node on the network contains data which is not known by its peers. So, each node acts as a data store to which other nodes can read or write. This enables the network to act as a distributed file-system. The data on each node is encrypted and is not known to other nodes providing anonymity.

 * Each node has a routing table which has the address of its neighbors and their performance in returning particular keys. The performance is assessed by its response time and transfer time. When a node receives a request, it searches its routing table to find the node which has been most successful in returning a similar key before. It is the keys which are used for finding relevance and not the files.

 * There are many advantages in this type of routing, such as anonymity, improved efficiency over time etc. But the drawback is that the network is slow as the data must pass through all the intermediate nodes.

 * The query routing flow in a Freenet algorithm is discussed in figure-4.

 * [[Image:fig4-routing.png|500px]]

 * iv. Semantic Routing
 * Semantic routing is a method which is more focused on the nature of query to be routed and it does not count on the network topology. This algorithm prioritizes the nodes which have been previously good at providing information about the types of content referred to by the query.

 * In order to search for information in a peer to peer network using semantic routing, the data needs to have some semantic description associated with it. This usually is the metadata information for the data. Tagging the data improves the semantic nature in a peer-to-peer network.

 * This algorithm modifies the confidence on a particular peer if it answers a query. If the node has answered the query correctly, then the confidence measure is increased. There must be a constant identifier in the network’s namespace to retain the confidence ratings. This is done in order to achieve persistence.

For Students

 * Le-Shin Wu, Ruj Akavipat, Filippo Menczer. 2005. Adaptive Query Routing in Peer Web Search. ACM, 1074-1075. DOI= http://doi.acm.org/10.1145/1062745.1062875.

 * Arturo Crespo and Hector Garcia-Molina. 2002. Routing Indices For Peer-to-Peer Systems. Proceedings of the 22 nd International Conference on Distributed Computing Systems (ICDCS'02).  IEEE Press. 23. DOI=http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.123.8939

 * Miguel Castro Peter, Peter Druschel,  Ayalvadi Ganesh,  Antony Rowstron,  Dan S. Wallach. 2002. Secure routing for structured peer-to-peer overlay networks. 299-314. ACM. DOI=http://doi.acm.org/10.1145/1060289.1060317

 * Ling Liu. 1999. Query Routing in Large-scale Digital Library Systems. Computer Society. International Conference on Data Engineering (ICDE'99). IEEE Press. 154. DOI=http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.10.6346

 * Wai Gen Yee, Linh Thai Nguyen, Dongmei Jia, Ophir Frieder. 2008. Efficient query routing by improved peer description in P2P networks. International conference on Scalable information systems. (ICST’08). DOI=http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.143.4994

For Faculty

 * A. Sugiura and O. Etzioni. 2000. Query Routing for Web Search Engines: Architecture and Experiments. North-Holland Publishing Co. 417 – 429. DOI = http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.79.522

 * Anjali Gupta, Barbara Liskov, Rodrigo Rodrigues. 2004. Efficient Routing for Peer-to-Peer Overlays. Proceedings of the 1st conference on Symposium on Networked Systems Design and Implementation - Volume 1. USENIX Association. 9. DOI = http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1.5939

 * Gibbins, N. and Hall, W. 2001. Scalability Issues for Query Routing Service Discovery. Proceedings of the Second Workshop on Infrastructure for Agents, MAS and Scalable MAS. 209-217. DOI= http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.28.7362

 * Abhishek Kumar, Jun (Jim) Xu, Ellen W. Zegura. 2005. Efficient and scalable query routing for unstructured peer-to-peer networks. 24th Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings IEEE. 1162- 1173. DOI= 10.1109/INFCOM.2005.1498343

Exercises / Learning activities
a) Class Activity - Adaptive Query Routing demonstration (20 minutes)

Visit the link below to view the online demonstration on Adaptive Query Routing: http://www.cc.gatech.edu/projects/disl/QR/

This is the homepage of the query routing software. AQR is an adaptive middleware layer that uses several query routing mechanisms and provides architecture for deploying them in an open networked environment. This online routing system was developed by Associate Professors Ling Liu and Carlton Pu with the help of graduate students David Buttler, Wei Han, Henrique Paques, and Wei Tang.

Select the ‘walkthrough’ section on the left panel. It navigates the user to the demo of query routing system. It provides an interactive demo of query routing aiding to understand the basic concepts and process involved in it. Break the class into 3-4 groups.

 * i) See the step-by-step process of query routing shown in walkthrough. Each group has to analyze and relate it to the techniques of query routing discussed in the syllabus 9.V. (Hint: This system uses the process of query refinement and source selection.)

 * ii) Each group prepares a report on what happens in each stage of the routing process.

b) In-class Exercise - Analysis of Freenet (20 minutes)

Visit http://freenetproject.org/ which contains the Freenet application. This is based on peer to peer routing model.

Read the routing technique used in Freenet. Download the software and try it (This is optional because the software might consume a lot of disk space).

 * i) Identify the need for using routing in this application.
 * ii) What type of routing technique does this system employ?
 * iii) What is the purpose of using key-based algorithm in this application?
 * iv) How is anonymity achieved in this peer to peer model?
 * v) Identify the advantages and drawbacks of this model.

Prepare a report on the above questions.

More details on Freenet routing can be found in http://freenetproject.org/ngrouting.html

c) Class Activity – Compare and Contrast (15 minutes)

Compare and contrast the approaches used in 12 (a) and 12 (b). 12 (a) is a class activity on Adaptive Query Routing demonstration and 12 (b) is an exercise about Freenet. Both these systems follow different methodologies. In class, break the students into groups of 3 or 4. Have them discuss the differences in the methodologies used by AQR and Freenet. Identify the advantages and disadvantages of the methods. Let each group prepare a Word document or PowerPoint slides (5 minutes) and presents it to the class (10 minutes).

Evaluation of learning outcomes
In their answers to the discussion questions, the students demonstrate an understanding of:
 * The need for routing systems
 * The basic concepts of routing
 * Different types and methodologies of routing techniques
 * Challenges involved in routing queries

Glossary
DRS: Digital Reference Service

Clustering: grouping of documents which satisfy a set of common properties. The aim is to assemble together documents which are related among them. Clustering can be used, for instance, to expand a user query with new and related index terms.

Scalability: The ability and ease with which a service may increase in size; where size may be defined as number of users, throughput of questions and responses, etc.

Vector model: an algebraic model for representing text documents (and any objects, in general) as vectors of identifiers. It is used in information filtering, information retrieval, indexing and relevancy rankings.

Additional useful links

 * Adaptive Query Routing at http://www.cc.gatech.edu/projects/disl/QR/

 * Freenet at http://freenetproject.org/

 * Chord at http://pdos.csail.mit.edu/chord/

 * Peer to peer routing at http://ntrg.cs.tcd.ie/undergrad/4ba2.05/group6/index.html

Contributors
Authors:
 * Venkatasubramaniam Ganesan
 * Dr. Edward Fox

Evaluators:
 * Ashwin Palani
 * Ashwin Khandeparker
 * Seungwon Yang

title: Digital Libraries/Routing


text:
University of Canberra/Generic skills

 * Generic skills and attributes of University of Canberra graduates from undergraduate and postgraduate coursework courses

The University of Canberra is committed to graduating creative professionals who are capable of developing innovative solutions to problems facing society. This will require graduates to possess analytical and critical capacities as well as the ability to synthesise ideas and adapt to new situations. The University expects its graduates to have developed a body of professional and disciplinary knowledge and technical skills as well as a set of appropriate professional attitudes. In addition, the University strives to develop in its students a range of skills and attributes which will equip them to succeed in a wide range of different tasks and jobs and facilitate their contribution to the society they live in.

The development of these skills and attributes will take place in all courses in ways that reflect the particular discipline or professional field. While the skills and attributes have been listed separately it should be recognised that effective professional practice requires the ability to integrate knowledge and the different skills and attributes.

The implementation of the generic skills and attributes
The skills and attributes expected of UC graduates are expressed in general or generic terms that provide a framework for their development in the context of different award levels and specific disciplines and professional fields. In particular, the skills and attributes will:
 * 1) reflect the focus on professional education at the University of Canberra;
 * 2) be supported and understood by all staff and students of the University;
 * 3) be fundamental to curriculum design, teaching methods and assessment within subjects and courses;
 * 4) contextualised at the level of individual subjects and courses;
 * 5) be relatable to the teaching and learning outcomes of particular subjects and courses;
 * 6) be explicitly communicated to staff, students and employers through subject and course documentation;
 * 7) be capable of self-assessment by students;
 * 8) be demonstrable through presentation of appropriate evidence by students; and
 * 9) be regularly reviewed.

The generic skills and attributes
The University recognises that individuals entering its programs bring with them a diversity of personal and professional attributes that should be further developed by their experience as students and graduates of the University. The University expects all of its graduates in undergraduate and postgraduate coursework courses to have developed the following skills and attributes to a level appropriate for their award which will equip them for success in professional life:

Communication
Graduates are expected to be able to:
 * 1) express knowledge, ideas and opinions in their professional field, both orally and in written form, with confidence and clarity;
 * 2) present arguments and ideas effectively;
 * 3) actively listen and respond to the ideas of other people;
 * 4) negotiate effectively; and
 * 5) create and present new ideas.

Information Literacy and Numeracy
Graduates are expected to be able to locate, identify, collate, analyse, manipulate, evaluate, interpret and present information and numerical data.

Information and Communication Technology
Graduates are expected to be able to select and use appropriate information and communication technology to retrieve, manipulate and present information.

Problem Solving
Graduates are expected to be able to:
 * 1) identify problems and analyse the main features of problems relevant to their professional field;
 * 2) apply appropriate problem solving processes, arguments, critical and creative thinking;
 * 3) implement and evaluate strategies for the resolution of problems;
 * 4) anticipate and define new problems; and
 * 5) identify and resolve new problems in new fields.

Working With Others
Graduates are expected to be able to:
 * 1) work with others as part of a group;
 * 2) take responsibility for carrying out agreed tasks;
 * 3) be aware of the different roles and responsibilities of group members;
 * 4) evaluate group performance;
 * 5) take initiative and demonstrate leadership; and
 * 6) respect the rights of others irrespective of their cultural background, race or gender.

Effective Workplace Skills
Graduates are expected to:
 * 1) demonstrate entrepreneurial skills including creativity, initiative, adaptability, leadership, resourcefulness;
 * 2) have the ability to initiate new ideas, implement decisions and cope with uncertainty; and
 * 3) be able to function in a multi-cultural or global environment.

Professional Ethics
Graduates are expected to:
 * 1) act responsibly, ethically and with integrity in the context of their profession and their obligations to society; and
 * 2) appreciate the social and cultural context of their profession.

Social Responsibility
Graduates are expected to:
 * 1) work toward improvement in society;
 * 2) understand economic, political, social, and environmental systems with an international perspective;
 * 3) act in environmentally sustainable ways; and
 * 4) accept service to the community as the primary purpose for professional life.

Lifelong Learning
Graduates are expected to:
 * 1) be independent self-directed learners with the capacity and motivation for lifelong learning;
 * 2) be aware of how they best learn;
 * 3) possess self-knowledge and the ability to assess their own performance critically and accurately; and
 * 4) have an understanding of how to apply their knowledge and abilities to many different contexts and fields.

Personal Attributes
Graduates are expected to:
 * 1) be independent thinkers and agents for change;
 * 2) have confidence to challenge existing ideas;
 * 3) show commitment to ongoing self-development;
 * 4) value and respect differing views;
 * 5) be confident in themselves and their own skills and knowledge.

title: University of Canberra/Generic skills


text: File:Heat 1d neu bc setup.ogv

title: File:Heat 1d neu bc setup.ogv


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Wikiversity open letter project/WMF Board March 2010
Hi there. Please note that I've reverted your recent changes for the reasons given in my edit summary. Whilst I think your observations about some of those who have signed this letter are interesting, adding such observations to the body of the letter, particularly after it has been sent probably isn't appropriate. You might wish to make comments about the signatories either in the discussion section on that page or on the talk page. Regards. Adambro 16:19, 9 April 2010 (UTC)

title: User talk:Cimon Avaro


text:
User talk:Square~enwikiversity

 Hello Square, and welcome to Wikiversity! If you need help, feel free to visit my talk page, or contact us and ask questions. After you leave a comment on a talk page, remember to sign and date; it helps everyone follow the threads of the discussion. The signature icon in the edit window makes it simple. To get started, you may

 * Take a guided tour and learn to edit.
 * Visit a (kind of) random project.
 * Browse Wikiversity, or visit a portal corresponding to your educational level: pre-school, primary, secondary, tertiary, non-formal education.
 * Find out about research activities on Wikiversity.

 * Read an introduction for teachers and find out how to write an educational resource for Wikiversity.
 * Give feedback about your initial observations
 * Discuss Wikiversity issues or ask questions at the colloquium.
 * Chat with other Wikiversitans on #wikiversity-en.

And don't forget to explore Wikiversity with the links to your left. Be bold to contribute and to experiment with the sandbox or your userpage, and see you around Wikiversity! If you're a twitter user, please follow http://twitter.com/Wikiversity. --Ottava Rima (talk) 16:55, 9 April 2010 (UTC)

Your account will be renamed
Hello,

The developer team at Wikimedia is making some changes to how accounts work, as part of our on-going efforts to provide new and better tools for our users like cross-wiki notifications. These changes will mean you have the same account name everywhere. This will let us give you new features that will help you edit and discuss better, and allow more flexible user permissions for tools. One of the side-effects of this is that user accounts will now have to be unique across all 900 Wikimedia wikis. See the announcement for more information.

Unfortunately, your account clashes with another account also called Square. To make sure that both of you can use all Wikimedia projects in future, we have reserved the name Square~enwikiversity that only you will have. If you like it, you don't have to do anything. If you do not like it, you can pick out a different name.

Your account will still work as before, and you will be credited for all your edits made so far, but you will have to use the new account name when you log in.

Sorry for the inconvenience.

Yours, Keegan Peterzell Community Liaison, Wikimedia Foundation 00:02, 18 March 2015 (UTC)

Renamed
 This account has been renamed as part of single-user login finalisation. If you own this account you can |log in using your previous username and password for more information. If you do not like this account's new name, you can choose your own using this form after logging in: . -- Keegan (WMF) (talk) 06:23, 19 April 2015 (UTC)

title: User talk:Square~enwikiversity


text:
File:Tracking Report Insert Final Version - Unit 1 Star Stuff.pdf

This is an assessment rubric based on the BC PLOs (Provincial Learning Outcomes) for unit one. We organized the final unit test by the same PLOs and then checked each box.


text: File:Learning Outcomes Reporting Document Science 9-1-.pdf


text:
User:Egm6341.s10.Team4.nimaa&m/HW7

= Problem 2: Analytical equation for $$\displaystyle x(t) $$ in Newton-Raphson method =

Given
The relationship between population growth $$\displaystyle x $$ respect to time $$\displaystyle t $$ can be expressed as follows:

 * {| style="width:100%" border="0" align="left"

$$ $$
 * $$\displaystyle
 * $$\displaystyle
 * $$\displaystyle \dot x=\frac{dx}{dt}=rx(1-\frac{x}{x_{max}})

 * }
 * }

Find
Find the analytical equation to show $$\displaystyle x $$ as a function of time $$\displaystyle t $$:

Solution

 * $$\displaystyle

\dot x=\frac{dx}{dt}=rx(1-\frac{x}{x_{max}}) $$

 * $$\displaystyle

\Rightarrow \frac{dx}{rx(1-\frac{x}{x_{max}}})=dt $$

For LHS we have:

 * $$\displaystyle

\frac{1}{x(1-\frac{1}{x_{max}}x)}=\frac{a}{x}+\frac{b}{1-\frac{1}{x_{max}}x}=\frac{a-\frac{a}{x_{max}}x+bx}{x(1-\frac{1}{x_{max}}x)} $$

 * $$\displaystyle

\Rightarrow a-\frac{a}{x_{max}}x+bx=1\Rightarrow x(b-\frac{a}{x_{max}})+a=1 $$

 * $$\displaystyle

\Rightarrow a=1 $$

 * $$\displaystyle

\Rightarrow b-\frac{a=1}{x_{max}}=0\Rightarrow b=\frac{1}{x_{max}} $$

 * $$\displaystyle

\Rightarrow \frac{1}{x(1-\frac{1}{x_{max}}x)}=\frac{1}{x}+\frac{\frac{1}{x_{max}}}{1-\frac{1}{x_{max}}x} $$

 * $$\displaystyle

\Rightarrow \frac{1}{r}\int\limits_{x_0}^{x}\frac{1}{x(1-\frac{1}{x_{max}}x)}\, dx=\frac{1}{r}\bigg[\int\limits_{x_0}^{x}\frac{1}{x}\,dx+\frac{1}{x_{max}}\int\limits_{x_0}^{x}\frac{1}{1-\frac{1}{x_{max}}x}\,dx\bigg]=\int\limits_{t_0}^{t}\,dt $$

 * $$\displaystyle

\Rightarrow \frac{1}{r}\bigg[ln(x)-ln(1-\frac{1}{x_{max}}x)]\Bigg ]^{x}_{x_0}=\frac{1}{r}\bigg[ln(\frac{x}{1-\frac{x}{x-{max}}})-ln(\frac{x_0}{1-\frac{x_0}{x_{max}}})\Bigg]=\int\limits_{t_0}^{t}\,dt $$

 * $$\displaystyle

\Rightarrow \frac{1}{r}ln(\frac{\frac{x}{1-\frac{x}{x_{max}}}}{\frac{x_0}{1-\frac{x_0}{x_{max}}}})=\frac{1}{r}ln(\frac{x(1-\frac{x_0}{x_{max}})}{x_0(1-\frac{x}{x_{max}})})=\int\limits_{t_0}^{t}\, dt=t-t_0 $$

Initial condition:

 * $$\displaystyle

t_0=0\Rightarrow x=x_0. $$

 * $$\displaystyle

\Rightarrow ln(\frac{x(1-\frac{x_0}{x_{max}})}{x_0(1-\frac{x}{x_{max}})})=r(t-t_0) $$

 * $$\displaystyle

\Rightarrow e^{rt}=\frac{x(1-\frac{x_0}{x_{max}})}{x_0(1-\frac{x}{x_{max}})}=\frac{x}{1-\frac{x}{x_{max}}}\times\frac{1-\frac{x_0}{x_{max}}}{x_0}=\frac{x_{max}.x}{x_{max}-x}\times\frac{x_{max}-x_0}{x_{max}.x_0} $$

 * $$\displaystyle

\Rightarrow \frac{x^2_{max}.x-x_{max}.x_0.x}{x^2_{max}.x_0-x_{max}.x_0.x}=e^{rt} $$

 * $$\displaystyle

\Rightarrow (x^2_{max}.x-x_{max}.x_0.x)=(x^2_{max}.x_0-x_{max}.x_0.x)e^{rt} $$

 * $$\displaystyle

\Rightarrow x(x^2_{max}-x_0.x_{max})=(x^2_{max}.x_0)e^{rt}-x_{max}.x_0.e^{rt}.x $$

 * $$\displaystyle

\Rightarrow x(x^2_{max}-x_0.x_{max}+x_{max}.x_0.e^{rt})=x^2_{max}.x_0.e^{rt} $$

 * $$\displaystyle

\Rightarrow x=\frac{x^2_{max}.x_0.e^{rt}}{x^2_{max}-x_0.x_{max}+x_{max}.x_0.e^{rt}}=\frac{x_{max}.x_0.e^{rt}}{x_{max}-x_0+x_0.e^{rt}} $$

 * {| style="width:１０0%" border="0" align="left"

$$\displaystyle \Rightarrow x(t)=\frac{x_0.x_{max}.e^{rt}}{x_{max}+x_0(e^{rt}-1)} $$
 * style="width:20%; padding:10px; border:2px solid #8888aa" |
 * style="width:20%; padding:10px; border:2px solid #8888aa" |
 * style= |
 * }.
 * }.

Author
Solved and typed by - Egm6341.s10.Team4.nimaa&m 15:50, 11 April 2010 (UTC) .

= Problem 8: Rate of momentum change for optimal control problem =

Given
Envisage the below figure as free body diagram of an aircraft:

Find
Show that $$\displaystyle \bar{CD}=\bar{AB} $$ + higher order terms of $$\displaystyle d\gamma $$.

Solution
According to the above figure, we can survey these two cases at $$\displaystyle t $$ and $$\displaystyle t+dt $$, the velocity of the aircraft after $$\displaystyle dt $$ will reach to $$\displaystyle v+dv $$ and the angle between the aircraft and horizontal axis will reach to the $$\displaystyle \gamma+d\gamma $$. Thus, for two generated triangles $$\displaystyle \triangle OAB $$ and $$\displaystyle \triangle ODC $$ following relationships can be written:

 * $$\displaystyle

tan(d\gamma)=\frac{\bar {AB}}{\bar {OA}}=\frac{\bar {AB}}{v} $$

Likewise we have below relationship in $$\displaystyle \triangle ODC $$:

 * $$\displaystyle

sin(d\gamma)=\frac{\bar {CD}}{\bar {OD}}=\frac{\bar {CD}}{v+dv} $$

Hence, by applying Taylor series for these two cases:

 * $$\displaystyle

tan(d\gamma)=d\gamma+\frac{d\gamma^3}{3}+\frac{2d\gamma^5}{15}+...=\frac{\bar{AB}}{v} $$

 * $$\displaystyle

\Rightarrow \bar{AB}=v(d\gamma+\frac{d\gamma^3}{3}+...)=v.d\gamma+v\frac{d\gamma^3}{3}+v\frac{2d\gamma^5}{15}+... $$

 * $$\displaystyle

sin(d\gamma)=d\gamma-\frac{d\gamma^3}{3!}+\frac{d\gamma^5}{5!}+...=\frac{\bar{CD}}{v+dv} $$

 * $$\displaystyle

\Rightarrow \bar{CD}=(v+dv)(d\gamma-\frac{d\gamma^3}{3!}+\frac{d\gamma^5}{5!}...)=v.d\gamma-v\frac{d\gamma^3}{3!}+v\frac{d\gamma^5}{5!}+... $$

 * $$\displaystyle

\Rightarrow \bar{CD}-\bar{AB}=-\frac{1}{2}vd\gamma^3-\frac{1}{8}vd\gamma^5... $$

 * {| style="width:１０0%" border="0" align="left"

$$\displaystyle \Rightarrow \bar{CD}=\bar{AB}+v.f(d\gamma^3)+v.f(d\gamma^5)+... $$
 * style="width:20%; padding:10px; border:2px solid #8888aa" |
 * style="width:20%; padding:10px; border:2px solid #8888aa" |
 * style= |
 * }.
 * }.

Author
Solved and typed by - Egm6341.s10.Team4.nimaa&m 22:19, 22 April 2010 (UTC) .

= Problem 14: Obtaining $$\displaystyle a_k $$ in cosine fourier transform =

Given
The Cosine series as Fourier transform for function $$\displaystyle f(x) $$ is expressed as follows:

 * {| style="width:100%" border="0" align="left"

$$ $$
 * $$\displaystyle
 * $$\displaystyle
 * $$\displaystyle f(cos\theta)=\frac{a_0}{2}+\sum_{k=1} a_k.cos(k\theta)

 * }
 * }

Find
Demonstrate the following expression for $$\displaystyle a_k $$:

 * {| style="width:100%" border="0" align="left"

$$ $$
 * $$\displaystyle
 * $$\displaystyle
 * $$\displaystyle a_k=\frac{2}{\pi}\int\limits_{0}^{\pi} f(cos\theta).cos(k\theta)\, d\theta

 * }
 * }

Solution
Considering $$\displaystyle x=cos(\theta) $$, cosine Fourier series for $$\displaystyle f(x) $$ is written as:

 * $$\displaystyle

f(x=cos(\theta))=\frac{a_0}{2}+\sum_{k=1} a_k.cos(k\theta) $$

 * $$\displaystyle

\xrightarrow{\times cos(k\theta)}f(x).cos(k\theta)=\frac{a_0}{2}.cos(k\theta)+\sum_{k=1} a_k.cos^2(k\theta) $$

 * $$\displaystyle

\xrightarrow{\int\limits_{0}^{\pi}}\int\limits_{0}^{\pi}f(x).cos(k\theta)\, d\theta=\frac{a_0}{2}\int\limits_{0}^{\pi}cos(k\theta)\, d\theta+\sum_{k=1} a_k\int\limits_{0}^{\pi}cos^2(k\theta)\, d\theta $$

 * $$\displaystyle

\frac{a_0}{2}\times\underbrace{\frac{1}{k}[sin(k\theta)]_{0}^{\pi}}_{=0}+\sum_{k=1}a_k\int\limits_{0}^{\pi}cos^2(k\theta)\, d\theta=\sum_{k=1}a_k\int\limits_{0}^{\pi}cos^2(k\theta)\, d\theta $$

 * $$\displaystyle

cos(2\theta)=cos^2(\theta)-sin^2(\theta)=2cos^2(\theta)-1\Rightarrow cos^2(\theta)=\frac{cos(2\theta)+1}{2} $$

 * $$\displaystyle

\Rightarrow \int\limits_{0}^{\pi}f(x).cos(k\theta)\, d\theta=\sum_{k=1} \lbrack a_k\times\frac{1}{2}\int\limits_{0}^{\pi}(cos(2\theta)+1)\, d\theta\rbrack=\frac{1}{2}\sum_{k=1}a_k\lbrack\int\limits_{0}^{\pi}cos(2\theta)\, d\theta+\int\limits_{0}^{\pi}\, d\theta]=\frac{1}{2}\sum_{k=1}a_k\lbrack[\underbrace{\frac{1}{2}sin(2\theta)]_{0}^{\pi}}_{=0}+\underbrace{[\theta]_{0}^{\pi}}_{\pi}\rbrack=\frac{\pi}{2}\sum_{k=1}a_k $$

 * $$\displaystyle

\Rightarrow a_k=\frac{2}{\pi}\int\limits_{0}^{\pi}f(x).cos(k\theta)\, d\theta $$

 * {| style="width:１０0%" border="0" align="left"

$$\displaystyle a_k=\frac{2}{\pi}\int\limits_{0}^{\pi}f(x).cos(k\theta)\, d\theta $$
 * style="width:20%; padding:10px; border:2px solid #8888aa" |
 * style="width:20%; padding:10px; border:2px solid #8888aa" |
 * style= |
 * }.
 * }.

Author
Solved and typed by - Egm6341.s10.Team4.nimaa&m 19:15, 18 April 2010 (UTC) .

title: User:Egm6341.s10.Team4.nimaa&m/HW7


text:
User:A. B./monobook.js

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title: User:A. B./monobook.js


text: File:Interplanetary Adventure Company.pdf


text:
Science Grade 9/Earth and Space/Big Bang and the production and organization of matter/Lesson Plans

Big Bang video (10 min)
 * Big Bang video (funny song) []


text:
Science Grade 9/Earth and Space/Big Bang and the production and organization of matter

(a)	 Lesson Plans

(b)	Labs/Activities

(c)	Assessments


text:
Instructional design/Introduction to Kirkpatrick’s four levels of evaluation/Level One Evaluation Item Types

Introduction
Now that you have identified the level 1 target areas, let's proceed to the next step in the process of creating your own survey.

What do the following have in common?

If you answered all of them are questions from a level 1 evaluation or reaction sheet, you are right! While there are many types of questions you can include in a level one evaluation, these survey questions fall into two main categories: closed-ended and open-ended.

Closed-ended questions
When you want learners to choose from a given set of possible responses that you have pre-determined, use this type of question. Closed-ended questions enable you to collect quantitative data that are easy to analyze after data collection. The main closed-ended questions used in a survey are Likert-scaled questions and multiple choice questions.

Likert-scaled questions
Likert-scaled questions allow learners to select a response from a given range of fixed choice responses containing a balanced number of positive and negative response that best matches their opinion. Remember to use this type of question when you want Learners to select only one response. The original Likert scale consists of five points, however there are other variations, such as a seven-point scale and six-point scale. Below is a typical five-point scale that measures agreement:

1—Strongly Disagree, 2—Disagree, 3—Neither Agree Nor Disagree, 4—Agree, 5—Strongly Agree.

You may also use variations of the Likert scale.

Example Innovate Technologies wants to find out what new employees think about their orientation program. Here are two examples of Likert-scaled questions (with slight variations in the scale) to include in a level one evaluation:

I found the orientation program's content easy to follow.

The trainer's presentation style kept me engaged.

Multiple choice questions
Multiple-choice questions allow learners to select one or more than one response from a given set of possible choices. When using this type of question, make sure all the answer choices are valid. When applicable, include “Other” as one of the choices. This type of question is useful when you want to find out what learners' top preferences are.

Example Stratford Healthcare wants to find out whether employees like the current duration of their IT training sessions or prefer other choices. Here’s an example of multi-choice question to include in a level one evaluation:

During an 8-hour session, I like: one 15-minute break and one hour-long break two 10-minute breaks and one 45-minute break three 20-minute breaks Others. Please specify:___________________

Open-ended questions
When you want to collect detailed feedback from your learners about a certain aspect of the training, use this type of question. Open-ended questions are a great way to collect qualitative data (i.e. data that does not include any numbers or figures). Use these type of questions to get descriptive input from participants, and their suggestions on key areas.


text:
Science Grade 9/Earth and Space/Structure of Atoms (Modern Atomic Theory)

(a)	Lessons
 * (Oak Bay School Website)
 * Youtube video "Atom Song":
 * Youtube video "Atom Song":

(b)	Labs/Activities

(c)	Assessments


text:
Science Grade 9/Earth and Space/Organization of Atoms (elements and the Periodic Table)

(a)	Lessons

 * (Oak Bay School Website)

(b)	Labs/Activities

(c)	Assessments


text:
Science Grade 9/Earth and Space/Formation and organization of simple molecules (Ionic Compounds)

(a)	Lessons
 * (Oak Bay School Website)

(b)	Labs/Activities

(c)	Assessments


text:
Science Grade 9

Developed as a curriculum guide for Grade 9 Science in British Columbia, Canada.

The Issue: The process of shifting what is happening in our schools toward 21st Century learning strategies and processes such as authentic and engaging learning, brain-based learning, assessment for learning, project-based learning, and systematic learning support strategies is time-intensive, expensive and slow even in schools that have made progress in becoming professional learning communities, devoted to improving learning and the assessment of learning. Wikipedia-style, broad-based collaborative development, which opens the development process to everyone in the province who has time, interest and expertise, has a tremendous potential to accelerate and enrich this effort. It may only require some group to take the initiative to get the ball rolling.

Goal: ‘Wikipedia’-style, online development to create course curricula with differentiated, SmartLearning-based lessons and learning strategies, differentiated Assessment for Learning strategies and processes, and a pyramid of intervention strategies to support both students who are not learning, and students who are learning well and need greater challenge. In time, it would be expected that there would be multiple versions of the curricula created, each meeting the course PLOs with different sets of themes, strategies and resource utilization such that the curricula would be tailored to the circumstances of a school, region, class or student.

/Characteristics of 21st Century Education/ (link)

Recent research in Education tells us that the best schools are those in which teaching and teaching assistant staff work productively together in teams on curriculum and teaching strategies on a regular, systematic basis. We meet monthly to plan and to help to promote promising innovative learning and teaching practices at Oak Bay. The Learning Team will focus on three strands this year:

•	Assessment for Learning (discussed below)

•	Differentiated Learning (strategies to meet individual learning needs)

•	Active, individualized Learning (strategies to make learning more flexible, individualized and engaging)

PHILOSOPHY •	each individual has a natural set of potentials and talents and interests

•	we learn constructively – by connecting the new with the known, in context (so ‘big picture’ questions and themes are important)

•	purposefully engaging emotions positively (expression, movement, stakes, novelty, staring, apprenticeships, thinking big)

•	teacher is coach – providing interesting learning opportunities, helpful feedback, and striving for max effective student self-direction (age and maturity appropriate)

•	focus on individualized learning in a continuous progress model with universal access supported by tech

•	we often forge meaning through social interaction and teamwork (talking, sharing and discussing)

•	stories and games are important: (learning and accomplishing goals within sets of rules)

•	this kind of education also naturally promotes the development of social responsibility

So, for this course, we are implementing the following:

Assessment for Learning

This involves a shift of the purpose of assessment from being primarily the way ‘to get grades’ for students, to being primarily a way of providing them feedback to help them to learn. This way, the focus is on the learning vs. the method of assessment, so that the ultimate goal is to master the course material vs. to ‘get a good grade’ (although both can be desirable).

Provincial Ministry of Education Prescribed Learning Outcomes

Active, individualized Learning with the use of performance assessments (projects): A three-year study of teaching and learning in more than 400 third-, sixth-, and eighth-grade classrooms in Chicago found that when students were given writing and mathematics assignments calling for more authentic work, they performed better on tests used to judge basic skills.

We find it helpful in developing a project to classify problems one of three ways: design a product, service, or system, improve one, or plan and stage an event. For maximum motivating power, the problem should be one adults face in a constructive field of human endeavor. When students tackle an actual problem in the community, by far the strongest learning occurs.

Link to more info on project-based learning

Effective use of Educational Technology We will also do our best to take advantage of available technology to support learning in our course:

•	Internet research

•	Wireless streaming video in the classroom

•	Use of online Science 9 course lessons and units to support remediation and enrichment

•	Student projects and presentations using PowerPoint and multimedia

title: Science Grade 9


text:
Science Grade 9/Earth and Space/Changes in the properties of matter (mass spectrometer and spectral analysis of stars)

(a)	Lessons

(b)	Labs/Activities

(c)	Assessments


text:
Science Grade 9/Earth and Space/Major Projects

 * CSI TV Entertainment YouTube video – Tragedy made glamorous:

 * Tragedy is not glamorous in real life YouTube video:

 * Balanced intro to challenges of CSI - real experts analyze fictional scenario YouTube video:


text: Science Grade 9/Earth and Space/Unit 1 Rubrics