The web is getting more and more characterized by personalization. Big socialnetworks like Facebook as well as the leading search engine Google increasingly usepersonalization algorithms to tailor the information that they present to users. All inorder to make the information more relevant and engaging for the end consumer. Howdoes this personalization affect journalists who increasingly search the web as a partof their journalistic research? In this essay we have looked at the effects thatpersonalization has on the journalists of tomorrow by conducting a survey amongstudents of journalism. We have also done a study of literature and theories to try anddetermine what consequences personalization will have on the internet in the future.We make an in depth study of the search engine Google as this is one of the mainsources of information for journalists and we have tried to tie this to theories of filter-bubbles and gatekeeping. We find that the question is in need of further studies toreally determine the threat that we face but conclude that information onpersonalization should be far more evident when it occurs.

The purpose of this thesis is to develop a heuristic solution for the static problem of resequencing unblocked vehicles as a part of an ongoing research project at Daimler AG. The target client of this project is Mercedes-Benz Cars. An unblocked vehicle is defined as a vehicle that for some reason could not be processed in its given time slot but at a later point in time needs to be inserted into the production sequence. Work overload is defined as work that the worker is unable to finish prior to reaching the station border. The resequencing problem can be described as finding new positions for a set of unblocked vehicles in a sequence of previously not blocked vehicles, such that the new sequence containing the previously not blocked vehicles and the additional unblocked vehicles causes as little work overload as possible. A decision has to be made in real-time, forcing the solution method to return a solution within a cycle time. Today, Mercedes-Benz Cars uses the sequencing approach “car sequencing”. This approach relies on so called spacing constraints, which basically means, trying to distribute work intensive vehicles as evenly as possible over the planning horizon and thereby enabling a hopefully smooth production. The car sequencing approach needs limited information. The difficulty is to find spacing constraints that fits the high level of product customization characterizing a modern car manufacturer. To overcome these difficulties, a new approach is being considered, namely the mixed-model sequencing, which takes more detailed data into account than the car sequencing approach but on the other hand is more costly in terms of computation. To this end, a simple but promising tabu search scheme was developed, that for many instances was able to find the optimal solution in less than 30 seconds of computing time and that also clearly outperformed all benchmark heuristics.

Queries submitted to search engines rarely provide a complete and precise description of a user's information need. Most queries are ambiguous to some extent, having multiple interpretations. For example, the seemingly unambiguous query ``tennis lessons'' might be submitted by a user interested in attending classes in her neighborhood, seeking lessons for her child, looking for online videos lessons, or planning to start a business teaching tennis. Search engines face the challenging task of satisfying different groups of users having diverse information needs associated with a given query. One solution is to optimize ranking functions to satisfy diverse sets of information needs. Unfortunately, existing evaluation frameworks do not support such optimization. Instead, ranking functions are rewarded for satisfying the most likely intent associated with a given query. In this thesis, we propose a framework and associated evaluation metrics that are capable of optimizing ranking functions to satisfy diverse information needs. Our proposed measures explicitly reward those ranking functions capable of presenting the user with information that is novel with respect to previously viewed documents. Our measures reflects quality of a ranking function by taking into account its ability to satisfy diverse users submitting a query. Moreover, the task of identifying and establishing test frameworks to compare ranking functions on a web-scale can be tedious. One reason for this problem is the dynamic nature of the web, where documents are constantly added and updated, making it necessary for search engine developers to seek additional human assessments. Along with issues of novelty and diversity, we explore one approximate approach to compare different ranking functions by overcoming the problem of lacking complete human assessments. We demonstrate that our approach is capable of accurately sorting ranking functions based on their capability of satisfying diverse users, even in the face of incomplete human assessments.

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In this project the optimization of transport planning has been studied. The approach was that smaller transport companies do not have the capability to fully optimize their transports. Their transport optimization is performed at a company level, meaning that the end result might be optimal for their company, but that potential for further optimization exists. The idea was to build a collaboration of transport companies, and then to optimize the transports globally within the collaboration. The intent was for the collaboration to perform the same driving assignments but at a lower cost, by using fewer vehicles and drivers, or travel shorter distance, or both combined. This should be achieved by planning the assignments in a smarter way, for example using a company's empty return journey to perform an assignment for another company. Due to the complexity of these types of problems, called Vehicle Routing Problem (VRP), shown to be NP-complete, search methods are often used. In this project the method of choice was a PDDL-based planner called LPG-td. It uses enforced hill-climbing together with a best-first search to find feasible solutions. The method was tested for scaling, performance versus another method and against time, as well as together with a real-life based problem. The results showed that LPG-td might not be a suitable candidate to solve the problem considered in this project. The solutions found for the collaboration were worse than for the sum of individual solutions, and used more computational time. Since the solution for the collaboration at most should be equal to the sum of individual solutions, in theory, this meant that the planner failed.

The creation of an Enterprise Search system involves many challenges that are not present in Web search. Searching a corporate collection is influenced both by the structure of the data present in the collection and by the policies of the corporation. These structures and policies may differ from corporation to corporation, and from collection to collection. In particular, an Enterprise Search system must take a document's genre into account. Examples of document genre within a corporate collection might include FAQs, white papers, technical reports, memos, emails and chat messages. Depending on an individual's current work task, it might be appropriate to give one genre a greater weight than another during the processing of a search request. Moreover, this weighting may change as the individual's work task changes. The work presented in this thesis adapts the Okapi BM25 scoring function to weight term frequency based on the relevance of a document genre to a work task. The method utilizes two user-provided resources, relevance judgments and clickthrough data, to estimate a realistic weight for each task-genre relationship. Using this approach, the method matches the purpose of each user search request with the purpose of each document. Therefore, the proper documents are returned to the user and her/his need can be fulfilled. The method has been incorporated into a prototype search engine, X-site, currently deployed on a corporate intranet. X-Site is a contextual search engine that uses the relationships between work tasks and document genres to improve search precision for software engineers. The system provides a customized and user-controlled means of refining search results to suit the task context of a user. Through X-Site, each employee can make a single search request and has access to documents from the Internet, a corporate intranet, and Lotus Notes databases.

Information retrieval has been a prevasive part of human society since its existence.With the advent of internet and World wide Web it became an extensive area of researchand major foucs, which lead to development of various search engines to locate the de-sired information, mostly for globally connected computer networks viz. internet.Butthere is another major part of computer network viz. intranet, which has not seen muchof advancement in information retrieval approaches, in spite of being a major source ofinformation within a large number of organizations.Most common technique for intranet based search engines is still mere database-centric. Thus practically intranets are unable to avail the benefits of sophisticated tech-niques that have been developed for internet based search engines without exposing thedata to commercial search engines.In this Master level thesis we propose a ”state of the art architecture” for an advancedsearch engine for intranet which is capable of dealing with continuously growing sizeof intranets knowledge base. This search engine employs lexical processing of doc-umetns,where documents are indexed and searched based on standalone terms or key-words, along with the semantic processing of the documents where the context of thewords and the relationship among them is given more importance.Combining lexical and semantic processing of the documents give an effective ap-proach to handle navigational queries along with research queries, opposite to the modernsearch engines which either uses lexical processing or semantic processing (or one as themajor) of the documents. We give equal importance to both the approaches in our design,considering best of the both world.This work also takes into account various widely acclaimed concepts like inferencerules, ontologies and active feedback from the user community to continuously enhanceand improve the quality of search results along with the possibility to infer and deducenew knowledge from the existing one, while preparing for the advent of semantic web.

Since the search engine was first released in 1993, the development has never been slow down and various search engines emerged to vied for popularity. However, current traditional search engines like Google and Yahoo! are based on key words which lead to results impreciseness and information redundancy. A new search engine with semantic analysis can be the alternate solution in the future. It is more intelligent and informative, and provides better interaction with users.        This thesis discusses the detail on semantic search, explains advantages of semantic search over other key-word-based search and introduces how to integrate semantic analysis with common search engines. At the end of this thesis, there is an example of implementation of a simple semantic search engine.

Three experiments were conducted to investigate the hypothesis that young infants? failures to search for occluded objects arises, not from deficiencies in their object representations, but from limitations in experience with the physical world. Successful means-end search is typically found at 8 months of age and is traditionally taken as the hallmark of object permanence. However, recent evidence suggests that infants much younger than 8 months of age are able to represent and reason about objects that are no longer visible. In Experiment 1, successful means-end search was found for 8. 5-, but not 6. 5-month-old infants in a traditional task, but younger infants showed successful search ability when the task was made familiar to them in Experiment 2 (i. e. , when the toy and occluder are first presented as a single composite object), and when they were given the opportunity to watch a demonstration of the solution to the task in Experiment 3. These results are taken as evidence for the ?experiential limitation? hypothesis and suggest that young infants are more apt at solving manual search tasks than previously acknowledged.

The creation of an Enterprise Search system involves many challenges that are not present in Web search. Searching a corporate collection is influenced both by the structure of the data present in the collection and by the policies of the corporation. These structures and policies may differ from corporation to corporation, and from collection to collection. In particular, an Enterprise Search system must take a document's genre into account. Examples of document genre within a corporate collection might include FAQs, white papers, technical reports, memos, emails and chat messages. Depending on an individual's current work task, it might be appropriate to give one genre a greater weight than another during the processing of a search request. Moreover, this weighting may change as the individual's work task changes. The work presented in this thesis adapts the Okapi BM25 scoring function to weight term frequency based on the relevance of a document genre to a work task. The method utilizes two user-provided resources, relevance judgments and clickthrough data, to estimate a realistic weight for each task-genre relationship. Using this approach, the method matches the purpose of each user search request with the purpose of each document. Therefore, the proper documents are returned to the user and her/his need can be fulfilled. The method has been incorporated into a prototype search engine, X-site, currently deployed on a corporate intranet. X-Site is a contextual search engine that uses the relationships between work tasks and document genres to improve search precision for software engineers. The system provides a customized and user-controlled means of refining search results to suit the task context of a user. Through X-Site, each employee can make a single search request and has access to documents from the Internet, a corporate intranet, and Lotus Notes databases.