Advances in Probabilistic Databases for Uncertain by John Grant, Francesco Parisi (auth.), Zongmin Ma, Li Yan

By John Grant, Francesco Parisi (auth.), Zongmin Ma, Li Yan (eds.)

This booklet covers a fast-growing subject in nice intensity and makes a speciality of the applied sciences and functions of probabilistic facts administration. It goals to supply a unmarried account of present stories in probabilistic information administration. the target of the booklet is to supply the state-of-the-art info to researchers, practitioners, and graduate scholars of data know-how of clever info processing, and while serving the data expertise expert confronted with non-traditional functions that make the appliance of traditional ways tough or impossible.

Show description

Read or Download Advances in Probabilistic Databases for Uncertain Information Management PDF

Best databases books

Microsoft Access Version

Triumph over Access—from the interior out! whats up, you recognize your means round Access—so now dig into model 2002 and very placed your databases to paintings! This award-winning, supremely prepared reference packs countless numbers of timesaving suggestions, troubleshooting guidance, and convenient workarounds in concise, fast-answer format—it’s all muscle and no fluff.

Oracle APEX 4.0 Cookbook

Over eighty nice recipes to advance and install quick, safe, and sleek net purposes with Oracle program exhibit four. zero Create feature-rich net functions in APEX four. zero combine third-party purposes like Google Maps into APEX by utilizing internet companies improve APEX purposes through the use of stylesheets, Plug-ins, Dynamic activities, AJAX, JavaScript, BI writer, and jQuery Hands-on examples to take advantage of out of the chances that APEX has to supply a part of Packt's Cookbook sequence: each one recipe is a gently geared up series of directions to accomplish the duty as successfully as attainable intimately Oracle software convey four.

From CA to CAS online: Databases in Chemistry

Considering that this e-book used to be first released in 1985, super adjustments have taken position within the box of on-line looking. hence a moment variation was once genuinely over­ due. Dr. Hedda Schulz, writer of the 1st variation, came upon a such a lot efficient and popular searcher as her co-author within the individual of Dr. Ursula Georgy.

Java Data Mining Strategy, Standard, and Practice A Practical Guide for architecture, design, and implementation

Even if you're a software program developer, platforms architect, information analyst, or company analyst, which will reap the benefits of facts mining within the improvement of complicated analytic functions, Java facts Mining, JDM, the hot average now carried out in middle DBMS and knowledge mining/analysis software program, is a key resolution part.

Extra info for Advances in Probabilistic Databases for Uncertain Information Management

Sample text

Natural language parsing. Parsing natural language consists in building syntax trees out of sentences. This is an uncertain operation, because of the complexity of the natural language, and its inherent ambiguity. Indeed, some sentences like “I saw her duck” have several possible syntax trees. A parser will typically rely on statistics gathered from corpora to assign probabilities to the different possible parse trees of a sentence [50]. This probability space of parse trees can then be seen as probabilistic XML data [18].

Also it is possible that c is a probabilistic class with fuzzy measure which contains attributes {a1, a2, …, ak, fpM1} and o be an object on attribute set {b1, b2, …, bl, fpM2}. Then without consideration of the fuzzy probabilistic attribute, the probability that o belongs to c is defined as follows. , k j i are included in the attribute domains of c. Considering the values of the fuzzy probabilistic attributes of o, the final degree that o belongs to c is defined as follows. ρc (o) = min (ρ’c (o), μ (o (fpM2), dom (fpM1)), o (fpM2)) The calculation of min (ρ’c (o), μ (o (fpM2), dom (fpM1)), o (fpM2)) also follows the coalescence operation of the fuzzy probabilistic attribute values in o1  o2 presented above.

Conflicts and their resolutions in fuzzy relational multidatabases. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 18(2), 169–195 (2010) 20. : Attribute and object selection queries on objects with probabilistic attributes. ACM Transactions on Database Systems 37(1), 3 (2012) 38 L. Yan and Z. Ma 21. : Current approaches to handling imperfect information in data and knowledge bases. IEEE Transactions on Knowledge and Data Engineering 8(2), 353–372 (1996) 22. : An algebra for probabilistic databases.

Download PDF sample

Rated 4.61 of 5 – based on 31 votes