Review of the mathematical foundations of data fusion techniques in surface metrology

The recent proliferation of engineered surfaces, including freeform and structured surfaces, is challenging current metrology techniques. Measurement using multiple sensors has been proposed to achieve enhanced benefits, mainly in terms of spatial frequency bandwidth, which a single sensor cannot provide. When using data from different sensors, a process of data fusion is required and there is much active research in this area. In this paper, current data fusion methods and applications are reviewed, with a focus on the mathematical foundations of the subject. Common research questions in the fusion of surface metrology data are raised and potential fusion algorithms are discussed

CRIS-IR 2006

The recognition of entities and their relationships in document collections is an important step towards the discovery of latent knowledge as well as to support knowledge management applications. The challenge lies on how to extract and correlate entities, aiming to answer key knowledge management questions, such as; who works with whom, on which projects, with which customers and on what research areas. The present work proposes a knowledge mining approach supported by information retrieval and text mining tasks in which its core is based on the correlation of textual elements through the LRD (Latent Relation Discovery) method. Our experiments show that LRD outperform better than other correlation methods. Also, we present an application in order to demonstrate the approach over knowledge management scenarios.Fundação para a Ciência e a Tecnologia (FCT) Denmark's Electronic Research Librar

Towards a Scalable Dynamic Spatial Database System

With the rise of GPS-enabled smartphones and other similar mobile devices, massive amounts of location data are available. However, no scalable solutions for soft real-time spatial queries on large sets of moving objects have yet emerged. In this paper we explore and measure the limits of actual algorithms and implementations regarding different application scenarios. And finally we propose a novel distributed architecture to solve the scalability issues.Comment: (2012

Human-Level Performance on Word Analogy Questions by Latent Relational Analysis

This paper introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, machine translation, and information retrieval. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason/stone is analogous to the pair carpenter/wood; the relations between mason and stone are highly similar to the relations between carpenter and wood. Past work on semantic similarity measures has mainly been concerned with attributional similarity. For instance, Latent Semantic Analysis (LSA) can measure the degree of similarity between two words, but not between two relations. Recently the Vector Space Model (VSM) of information retrieval has been adapted to the task of measuring relational similarity, achieving a score of 47% on a collection of 374 college-level multiple-choice word analogy questions. In the VSM approach, the relation between a pair of words is characterized by a vector of frequencies of predefined patterns in a large corpus. LRA extends the VSM approach in three ways: (1) the patterns are derived automatically from the corpus (they are not predefined), (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data (it is also used this way in LSA), and (3) automatically generated synonyms are used to explore reformulations of the word pairs. LRA achieves 56% on the 374 analogy questions, statistically equivalent to the average human score of 57%. On the related problem of classifying noun-modifier relations, LRA achieves similar gains over the VSM, while using a smaller corpus

New IR & Ranking Algorithm for Top-K Keyword Search on Relational Databases ‘Smart Search’

Database management systems are as old as computers, and the continuous research and development in databases is huge and an interest of many database venders and researchers, as many researchers work in solving and developing new modules and frameworks for more efficient and effective information retrieval based on free form search by users with no knowledge of the structure of the database. Our work as an extension to previous works, introduces new algorithms and components to existing databases to enable the user to search for keywords with high performance and effective top-k results. Work intervention aims at introducing new table structure for indexing of keywords, which would help algorithms to understand the semantics of keywords and generate only the correct CN‟s (Candidate Networks) for fast retrieval of information with ranking of results according to user‟s history, semantics of keywords, distance between keywords and match of keywords. In which a three modules where developed for this purpose. We implemented our three proposed modules and created the necessary tables, with the development of a web search interface called „Smart Search‟ to test our work with different users. The interface records all user interaction with our „Smart Search‟ for analyses, as the analyses of results shows improvements in performance and effective results returned to the user. We conducted hundreds of randomly generated search terms with different sizes and multiple users; all results recorded and analyzed by the system were based on different factors and parameters. We also compared our results with previous work done by other researchers on the DBLP database which we used in our research. Our final result analysis shows the importance of introducing new components to the database for top-k keywords search and the performance of our proposed system with high effective results.نظم إدارة قواعد البيانات قديمة مثل أجيزة الكمبيوتر، و البحث والتطوير المستمر في قواعد بيانات ضخم و ىنالك اىتمام من العديد من مطوري قواعد البيانات والباحثين، كما يعمل العديد من الباحثين في حل وتطوير وحدات جديدة و أطر السترجاع المعمومات بطرق أكثر كفاءة وفعالية عمى أساس نموذج البحث الغير مقيد من قبل المستخدمين الذين ليس لدييم معرفة في بنية قاعدة البيانات. ويأتي عممنا امتدادا لألعمال السابقة، ويدخل الخوارزميات و مكونات جديدة لقواعد البيانات الموجودة لتمكين المستخدم من البحث عن الكممات المفتاحية )search Keyword )مع األداء العالي و نتائج فعالة في الحصول عمى أعمى ترتيب لمبيانات .)Top-K( وييدف ىذا العمل إلى تقديم بنية جديدة لفيرسة الكممات المفتاحية )Table Keywords Index ،)والتي من شأنيا أن تساعد الخوارزميات المقدمة في ىذا البحث لفيم معاني الكممات المفتاحية المدخمة من قبل المستخدم وتوليد فقط الشبكات المرشحة (s’CN (الصحيحة السترجاع سريع لممعمومات مع ترتيب النتائج وفقا ألوزان مختمفة مثل تاريخ البحث لممستخدم، ترتيب الكمات المفتاحية في النتائج والبعد بين الكممات المفتاحية في النتائج بالنسبة لما قام المستخدم بأدخالو. قمنا بأقتراح ثالث مكونات جديدة )Modules )وتنفيذىا من خالل ىذه االطروحة، مع تطوير واجية البحث عمى شبكة اإلنترنت تسمى "البحث الذكي" الختبار عممنا مع المستخدمين. وتتضمن واجية البحث مكونات تسجل تفاعل المستخدمين وتجميع تمك التفاعالت لمتحميل والمقارنة، وتحميالت النتائج تظير تحسينات في أداء استرجاع البينات و النتائج ذات صمة ودقة أعمى. أجرينا مئات عمميات البحث بأستخدام جمل بحث تم أنشائيا بشكل عشوائي من مختمف األحجام، باالضافة الى االستعانة بعدد من المستخدمين ليذه الغاية. واستندت جميع النتائج المسجمة وتحميميا بواسطة واجية البحث عمى عوامل و معايير مختمفة .وقمنا بالنياية بعمل مقارنة لنتائجنا مع االعمال السابقة التي قام بيا باحثون آخرون عمى نفس قاعدة البيانات (DBLP (الشييرة التي استخدمناىا في أطروحتنا. وتظير نتائجنا النيائية مدى أىمية أدخال بنية جديدة لفيرسة الكممات المفتاحية الى قواعد البيانات العالئقية، وبناء خوارزميات استنادا الى تمك الفيرسة لمبحث بأستخدام كممات مفتاحية فقط والحصول عمى نتائج أفضل ودقة أعمى، أضافة الى التحسن في وقت البحث