information retrieval system speed

The integrity of data stored on optical disks is also impressive. The process by which data is recorded on a Write-Once Read Many (WORM) disk surface causes an irreversible physical change. There is no chance of accidental erasure and overwrites are usually prevented by the drive or by driver software. Erasable optical disks cannot prevent accidental or malicious data destruction, but are still more durable then magnetic media. CD-ROM (Compact Disk Read Only Memory) disks are physically pressed out of plastic and so cannot be written to, or erased. Further, since there is no physical contact between an optical disk platter and the access mechanism, the disk is not subject to wear with use (the head of a Winchester type magnetic disk actually rides on the lubricated disk surface before it reaches flying speed). Small scratches and dust on the disk surface do not affect stored data as they are far enough away from the recording surface to be out of the optical system's focal plane. Data is also not subject to destruction from external magnetic fields and does not require periodic rewriting as is the case for magnetic media. The expected lifetime of an optical disk is not really known for certain (they have not been around long enough), but accelerated aging tests place it at least ten years and possibly as high as thirty. In contrast, the lifetime of magnetic tape is between two years to ten years, depending how it is handled and stored.

The data used in this work consisted of a set of historical days data which consisted of parameters that described daily weather and snowpack conditions. The amount of precipitation, depth of new snow and wind speed were typical of these parameters. These data differed in one significant way from those normally processed by a text based IR system: the parameters, representing each day, were of different types and therefore the measurement of similarity between days was more complex than the measurement of similarity between two documents. An IR system is, generally, not equipped to handle this multi-typed matching as the similarity of a query to a docu- ment is typically based on one feature type, i.e. words. Rather than adapt a system to accommodate the avalanche data, it was transformed to be compatible with those which the IR system processed.

Information Retrieval (IR for short) which finds information that actually need among large collection of documents is concerned with representing, searching, and manipulating large collections of electronic text and other human-language data [1]. Web search engines — Google, Bing, and others— are by far the most popular and heavily used IR services, providing access to up-to-date technical information, locating people and organizations, summarizing news and events, and simplifying comparison shopping[1]. The main aim of retrieval system is to provide most relevant documents to users. Therefore, giving relevant results, granting efficient and fast access is the main focus on the performance of retrieval system. The basic method of traditional IR is to find documents that contain the terms in the user query. Given a user query, one option is to scan the document database sequentially to find the documents that contain the query terms [1]. However, this method is obviously impractical for a large collection, such as the web. Another option is to build some data structures (called indices) from the document collection to speed up the search. IR model governs how a document and a query are represented and how the relevance of a document to a user query is defined [14]. There are four main models: Boolean model, vector space model, language model and probabilistic model [1][14]. But these models based on keyword or term matching, i.e., directly matches terms in the user query with those in the documents. If a user query uses different words from the words used in a document, the document will not be retrieved although it may be relevant because the document uses some synonyms of the words in the user query [1]. This causes low recall. For example, the words “doctor”, “physicians”, “surgeon” are synonyms in the context of “doctor”. If the system doesn’t consider these contexts of a word “doctor”, we all don’t have a chance to see many papers that use synonyms of the word that is used in users’ query. This leads to context semantic indexing. Since there are typically many ways to specify a given concept (synonymy), the literal terms in a user's query might not match those of a relevant document. Moreover, most words have multiple meanings (polysemy), therefore terms in a user's query can literally match terms in irrelevant

The Lovins Stemmer removes a maximum of one suffix from a word, due to its nature as single pass algorithm. It uses a list of about 250 different suffixes, and removes the longest suffix attached to the word, ensuring that the stem after the suffix has been removed is always at least 3 characters long. Then the ending of the stem may be reformed (e.g., by un-doubling a final consonant if applicable), by referring to a list of recoding transformations. J.B. Lovins, 1968: "Development of a stemming algorithm," Mechanical Translation and Computational Linguistics. This stemmer, though innovative for its time, has the problematic task of trying to please two masters (IR and Linguistics) and cannot excel at either. The approach does not excel with linguistics, as it is not complex enough to stem many suffixes due to their not being present in the rule list. This is interesting as Lovins‟ rule list was derived by, processing and studying a word sample. Perhaps if this process was repeated with a much larger sample a more satisfactory rule list could be derived. There are also known to be problems regarding the reformation of words. This process uses the recoding rules to reform the stems into words to ensure they match stems of other similar meaning words. The main problem with this process is that it has been found to be highly unreliable and frequently fails to form words from the stems, or match the stems of like meaning words. The Stemmer does not excel from the IR viewpoint either, as its large rule set, and its recoding stage, affect its speed of execution.

The Lovins Stemmer removes a maximum of one suffix from a word, due to its nature as single pass algorithm. It uses a list of about 250 different suffixes, and removes the longest suffix attached to the word, ensuring that the stem after the suffix has been removed is always at least 3 characters long. Then the ending of the stem may be reformed (e.g., by un-doubling a final consonant if applicable), by referring to a list of recoding transformations. J.B. Lovins, 1968: "Development of a stemming algorithm," Mechanical Translation and Computational Linguistics. This stemmer, though innovative for its time, has the problematic task of trying to please two masters (IR and Linguistics) and cannot excel at either. The approach does not excel with linguistics, as it is not complex enough to stem many suffixes due to their not being present in the rule list. This is interesting as Lovins‟ rule list was derived by, processing and studying a word sample. Perhaps if this process was repeated with a much larger sample a more satisfactory rule list could be derived. There are also known to be problems regarding the reformation of words. This process uses the recoding rules to reform the stems into words to ensure they match stems of other similar meaning words. The main problem with this process is that it has been found to be highly unreliable and frequently fails to form words from the stems, or match the stems of like meaning words. The Stemmer does not excel from the IR viewpoint either, as its large rule set, and its recoding stage, affect its speed of execution.

Processing of bibliographic information is mostly a string manipulation and matching operation. For this purpose, data processing and string manipulating lan- guages, such as COBOL, SNOBOL, PL/I, LISP, BASIC, etc are suitable. Using languages developed for scientific and numerical computation, such as FORTRAN, to biblio- graphic information processing has its own dis-

The result of the syntactic analysis is a grammatical analysis of the input sentence in the form of a so called o-form.. The construction of the o-form can be expressed ba the structure:[r]

maintain relationship manually with the students for providing the information about the drives which are going on. It is a time consuming task. The shortlisting process and recruitment process takes time and it is difficult to maintain the student data manually.

Security has become a prime concern for providing security while communication between mobile nodes in Mobile Ad Hoc networks (MANETs), due to its unique characteristics like rapid movement of node in infrastructure less network that‘s changes it‘s topology. In this paper we have discussed a threshold cryptography techniques based on Identity-Based information retrieval system in MANET which uses Citification authority for the purpose of accessing of nodes by using corresponding session key that is generated by langrage‘s polynomial to provide a safe strategy for authentication over Mobile Ad hoc Network.

Intelligent agents understand what we mean, find and use relevant information, automate routine tasks, adapt to our needs and desires, cooperate with other assistants. Intelligent Information Agents can be defined as computational software systems that access multiple heterogeneous and geographically distributed information sources in order to help users in the process of searching relevant information. These agents provide transparent access to many different information sources, simplifying the problem of managing a large amount of data. This implies that agents must interpret the user request, analyze it, translate it in the correct way for each source, extract the desired information, and gives the results back to the user.

Abstract: - Web is a huge source of informa tion a number of internet users visi t on different web si tes and extra ct thei r requi red da ta . Tha t is di rect source of informa tion whi ch is used by end client. On the other hand some additional data genera ted on the pa rked domain web server whi ch is used by web si te administra tor and used for deciding the future business trends and future servi ce planning. Tha t essential information is recovered from the web server l og files, knowledge extra ction from these raw files a re also called the web usage mining. In this presented work web usage mining is inves ti gated and a new da ta model for web recommenda tion is reported. in order to develop the proposed recommender s ys tem the user session web a ccessed log da ta is a ccessed and classified on the basis of the time based fashion. This kind of anal ysis demons tra tes the user web a ccess browsing beha viour in di fferent time slots. Thus a ccording to the user beha vi our anal ysis in different time domains a predi cti ve model namel y hidden Ma rkov model is a pplied on the recovered da ta . Tha t uses the probability es tima tion techniques for finding the new na vi ga tional web a ccess trend. The proposed da ta model is implemented using the visual s tudio envi ronment and the performance of the predicti ve algori thm is computed. The performance of the i mplemented s ys tem is evaluated in terms of a ccura cy, memory consumption, error ra te and time consumption. According to the obtained resul ts the p resented technique enhancing the performance as the training da ta is increases.

In Kanobe, the author worked Web-based blood donor management information system for the Red Cross Society, Uganda (WBBDMI), and the work which describes the development of a blood management information system which helps in the management of blood donor information and records and also controls blood supplies in various parts of Uganda based on hospital demands. 5 The Web-based donor blood management information system offers very important features, one of which is quick access to donor ’ s information collected from different parts of Uganda. This helps in monitoring the results and performance of blood donation activity such that the important objectives of the organization can be checked. The system provides the man- agement timely, con fi dential medical reports that aid plan- ning and decision making and thereby improving the medical service delivery.

Discussion: one of the reasons for the participants not doing browsing is due to the nature of the task, e.g. in the test each participant was asked to find four ads in the collection and before each search he was shown the ad he was going to find, which made it unnecessary for the participant to start search through browsing, whose main role is to help users with “muddled topical [information] needs, or ill- defined information problems” (Ingwersen, 86) to further clarify their questions in mind. Besides, even though participants were told that the purpose of the test was not to test their search skill, somehow they felt the time pressure to a certain degree and tried to finish the task as soon as possible.

Natural Language Processor is main part of the system where actual processing takes place. NLP Processor is a combination of Semantic Analysis technique and keyword based approach. Semantic Analysis is a choice when none of the words in the entered query matches with the keywords in the knowledge base. It means that either the query is invalid that it does not fit into the context of database that we have. Or there is a chance that some of the keywords are missing in the knowledge base even though it is a relevant query. If it is first case, Semantic Analysis also cannot produce the result. In case of second option, Semantic analysis will try to form the query using the linguistic relationship between the words from the Natural language query. The output of this step is intermediate query in case of Semantic analyzer and components of the query in case of Keywords based approach. This is done by using Stanford dependency parser. The details of dependency parser are given below. Stanford Dependency Parser

Abstract: In old days, people have turned out to be conscious about the outcomes of documenting and discovering information. With the arrival of computers, it became possible to store tremendous measure of information; and finding the valuable information from images collections which is turning into a need. Out of this need in the 1950s, the field of Information Retrieval (IR) was conceived. The field of information retrieval has developed extensively finished the most recent forty years. A few IR systems are utilized on a regular by a wide assortment of users. Information retrieval turns into an imperative research region in the field of software engineering. Information retrieval (IR) is generally concerned about the looking and recovering of relevant similar image information from database. In this paper, we propose a novel mechanism in combination of SIFT and Relevance Feedback algorithm for looking of similar image. The user queries based of image and system produces output of similar images with relevance feedback mechanism for development of retrieval.

System Programs 4.1 Storage and Retrieval of Information 4.1.1 File System 4.1.2 File Organization 4.1.3 Retrieval of Items from the List Structured File 4.1.4 Real Time Updating of the [r]

Precision and Recall are the two main evaluation metrics used in this type of retrieval system. These are basic measure and these are extended to serve the ranked retrieval system. This session gives an overview of the methods applied to assess the quality of search systems for the set of records obtained after performing a query in an information retrieval system.

To achieve this purpose, an information retrieval system should provide the translation of user’s keywords into controlled vocabulary terms, based on the subject headings of related thesaurus. There is no doubt that choosing the accurate and consistent keywords would provide the possibilities to enhance the content represen- tation and retrieval of related literature.

The tremendous growth of data generated by various resources like digital images, videos and scientific instruments & simulations is very high. The usage of social networks and internet are increasing day-by-day. The effective information retrieval from this huge volume of data is very much obligatory. The existing information retrieval like Boolean, Meta and Probabilistic are not sufficient to manipulate this huge volume of data. In this scenario, the entry of Cloud Technology is very much needed. In this paper a new cloud based information retrieval system is proposed with the inclusion of Vector Space Model and Semi-Supervised Clustering. KEGG Metabolic Relation Network and Dow Jones Industrial Index data set are experienced with the CCBIR system using math work. This system eliminates bottlenecks in information flow, time delay and also it provides a remarkable ability to overcome the traffic congestions.

This is because the Fire Safety Division has no general data base on past incidents therefore to obtain information of this kind, it is necessary either to look through the monthly or an[r]