“ A Developer’s Guide to Data Modeling for SQL Server explains the concepts and practice of data modeling with a clarity that makes the technology accessible to anyone building databases and data-driven applications.
“Eric Johnson and Joshua Jones combine a deep understanding of the science of data modeling with the art that comes with years of experience. If you’re new to data modeling, or find the need to brush up on its concepts, this book is for you.”
—Peter Varhol, Executive Editor, Redmond Magazine ...
We address the problem of selecting nondomain-speciﬁc language model training data to build auxiliary language models for use in tasks such as machine translation. Our approach is based on comparing the cross-entropy, according to domainspeciﬁc and non-domain-specifc language models, for each sentence of the text source used to produce the latter language model. We show that this produces better language models, trained on less data, than both random data selection and two other previously proposed methods. ...
This book is intended to introduce environmental scientists and
managers to the statistical methods that will be useful for them in their
work. A secondary aim was to produce a text suitable for a course in
statistics for graduate students in the environmental science area. I
wrote the book because it seemed to me that these groups should
really learn about statistical methods in a special way. It is true that
their needs are similar in many respects to those working in other
This Second Edition of the go-to reference combines the classical analysis and modern applications of applied mathematics for chemical engineers. The book introduces traditional techniques for solving ordinary differential equations (ODEs), adding new material on approximate solution methods such as perturbation techniques and elementary numerical solutions. It also includes analytical methods to deal with important classes of finite-difference equations. The last half discusses numerical solution techniques and partial differential equations (PDEs). The read...
Data Modeling Techniques for Data Warehousing
Chuck Ballard, Dirk Herreman, Don Schau, Rhonda Bell, Eunsaeng Kim, Ann Valencic
International Technical Support Organization http://www.redbooks.ibm.com
International Technical Support Organization
Data Modeling Techniques for Data Warehousing February 1998
.Take Note! Before using this information and the product it supports, be sure to read the general information in Appendix B, “Special Notices” on page 183.
We present a global joint model for lemmatization and part-of-speech prediction. Using only morphological lexicons and unlabeled data, we learn a partiallysupervised part-of-speech tagger and a lemmatizer which are combined using features on a dynamically linked dependency structure of words. We evaluate our model on English, Bulgarian, Czech, and Slovene, and demonstrate substantial improvements over both a direct transduction approach to lemmatization and a pipelined approach, which predicts part-of-speech tags before lemmatization. ...
Morphological processes in Semitic languages deliver space-delimited words which introduce multiple, distinct, syntactic units into the structure of the input sentence. These words are in turn highly ambiguous, breaking the assumption underlying most parsers that the yield of a tree for a given sentence is known in advance. Here we propose a single joint model for performing both morphological segmentation and syntactic disambiguation which bypasses the associated circularity.
The applicability of many current information extraction techniques is severely limited by the need for supervised training data. We demonstrate that for certain ﬁeld structured extraction tasks, such as classiﬁed advertisements and bibliographic citations, small amounts of prior knowledge can be used to learn effective models in a primarily unsupervised fashion. Although hidden Markov models (HMMs) provide a suitable generative model for ﬁeld structured text, general unsupervised HMM learning fails to learn useful structure in either of our domains.
In this paper, a new language model, the Multi-Class Composite N-gram, is proposed to avoid a data sparseness problem for spoken language in that it is difﬁcult to collect training data. The Multi-Class Composite N-gram maintains an accurate word prediction capability and reliability for sparse data with a compact model size based on multiple word clusters, called MultiClasses. In the Multi-Class, the statistical connectivity at each position of the N-grams is regarded as word attributes, and one word cluster each is created to represent the positional attributes. ...
We propose a novel reordering model for phrase-based statistical machine translation (SMT) that uses a maximum entropy (MaxEnt) model to predicate reorderings of neighbor blocks (phrase pairs). The model provides content-dependent, hierarchical phrasal reordering with generalization based on features automatically learned from a real-world bitext. We present an algorithm to extract all reordering events of neighbor blocks from bilingual data.
We present a generative distributional model for the unsupervised induction of natural language syntax which explicitly models constituent yields and contexts. Parameter search with EM produces higher quality analyses than previously exhibited by unsupervised systems, giving the best published unsupervised parsing results on the ATIS corpus. Experiments on Penn treebank sentences of comparable length show an even higher F1 of 71% on nontrivial brackets. We compare distributionally induced and actual part-of-speech tags as input data, and examine extensions to the basic model.
EUFID is a natural language frontend for data management systems. It is modular and table driven so that it can be interfaced to different applications and data management systems. It allows a user to query his data base in natural English, including sloppy syntax and misspellings. The tables contain a data management system view of the data base, a semantic/syntactic view of the application, and a mapping from the second to the first. We are entering a new era in data base access. Computers and terminals have come down in price while salaries have risen. ...
Lecture "Advanced Econometrics (Part II) - Chapter 10: Models for panel data" presentation of content: General framework for panel data, pooled regression, fixed effects, random effects model, choosing between fixed and random effects models, finding big.
The traditional mention-pair model for coreference resolution cannot capture information beyond mention pairs for both learning and testing. To deal with this problem, we present an expressive entity-mention model that performs coreference resolution at an entity level. The model adopts the Inductive Logic Programming (ILP) algorithm, which provides a relational way to organize different knowledge of entities and mentions.
Chapter 6 - Developing data models for business databases. Chapter 5 explained the Crow's Foot notation for entity relationship diagrams. You learned about diagram symbols, relationship patterns, generalization hierarchies, and rules for consistency and completeness. Understanding the notation is a prerequisite for applying it to represent business databases. This chapter explains the development of data models for business databases using the Crow's Foot notation and rules to convert ERDs to table designs.
Module 9 - Creating a security design for data. In this module, you will learn how to determine threats and analyze risks to data in an organization. You will learn how to design an access control model for files and folders in order to protect data that is stored on network servers. You will also learn about considerations for encrypting and managing data.
(BQ) In this paper, an easily implemented method is developed to describe the variations of EDM process, represented by gap states. On the basis of a time series of gap states from a machining process, the paper first studied a general descriptive model for EDM process, and then equivalently simplified the model for application; after spectral
analysis, preprocessing of data, parameters selection and model validation proposed a well-defined model. Finally, by using this model structure and size, an online time-varied predictive model was developed.
MTMF combines the best parts of the Linear Spectral Mixing model and the statistical
Matched Filter model while avoiding the drawbacks of each parent method (Boardman,
1998). It is a useful Matched Filter method without knowing all the possible endmembers in
a landscape especially in case of subtle, sub-pixel occurrences. Firstly, pixel spectra and
endmember spectra require a minimum noise fraction (MNF) (Green et al., 1988, Boardman,
1993) transformation. MNF reduces and separates an image into its most dimensional and
The Data-Link layer is the protocol layer in a program that handles the moving of data in and out across a physical link in a network. The Data-Link layer is layer 2 in the Open Systems Interconnect (OSI) model for a set of telecommunication protocols.The Data-Link layer ensures that an initial connection has been set up, divides output data into data frames, and handles the acknowledgements from a receiver that the data arrived successfully. It also ensures that incoming data has been received successfully by analyzing bit patterns at special places in the frames....