The presentation shows an approach to table understanding using a rule engine. The proposed approach can be applied to large-volume conversion of tabular data from unstructured to structured form. You can find more about our project at http://cells.icc.ru
The document provides an overview of key concepts in the relational model, including:
- Relations are represented as tables with tuples as rows and attributes as columns.
- Each attribute has a domain that restricts its values. The degree of a relation is the number of attributes.
- Primary keys uniquely identify tuples and cannot be null. Foreign keys match primary keys in other relations.
- Integrity rules like entity integrity enforced through primary keys and referential integrity enforced through foreign keys.
Relational Theory for Budding Einsteins -- LonestarPHP 2016Dave Stokes
This document provides an overview of relational database theory and normalization for developers. It defines key terms like relational databases, logical and physical data models, database schemas, and data normalization. It explains the concepts of first, second, third and Boyce-Codd normal forms and how to normalize data to these forms by removing redundant and unnecessary data through a multi-step process. The goal of normalization is to organize data to minimize duplication and ensure integrity. An example demonstrates normalizing a dog owner database from first to third normal form.
This document provides an overview of data modeling and transforming entity relationship diagrams into relational database tables. It defines key concepts like the data dictionary, relational schema diagram, entities, attributes, relationships and how these map to tables, rows, columns, primary keys and foreign keys. Constraints like entity integrity and referential integrity are also discussed. Examples are provided throughout to illustrate mapping different entity types like regular, weak, unary and binary relationships into normalized database tables.
This document provides a summary of questions and answers related to data structures from Anna University regulation papers from 2008 to 2013. It covers topics like linear data structures (lists, stacks, queues), non-linear data structures (trees), and abstract data types. The document is compiled by Dr. P. Subathra and contains questions from various regulation years with detailed explanations and examples for each question.
The document discusses the relational database model. It was introduced in 1970 and became popular due to its simplicity and mathematical foundation. The model represents data as relations (tables) with rows (tuples) and columns (attributes). Keys such as primary keys and foreign keys help define relationships between tables and enforce integrity constraints. The relational model provides a standardized way of structuring data through its use of relations, attributes, tuples and keys.
This document provides an overview of the relational data model and relational database concepts. It defines what a relational database is and how data is organized into tables with rows and columns. It describes key components like schemas and relational database management systems. The document also covers relational algebra operations like select, project, join, and set operations. Finally, it provides a basic introduction to the structured query language SQL, including some common SQL commands and how it is used to perform queries, updates, and other data operations on relational databases.
Oracle 9i is a client/server database management system based on the relational data model. It handles failures well through transaction logging and allows administrators to manage users and databases through administrative tools. SQL*Plus provides an interactive interface for writing and executing SQL statements against Oracle databases, while PL/SQL adds procedural programming capabilities. Common SQL statements retrieve, manipulate, define and control database objects and transactions.
Normalization is the process of organizing data in a database to minimize redundancy and dependency. It involves splitting tables and establishing relationships between them through primary and foreign keys. There are various normal forms that represent increasing levels of normalization, from 1NF to 3NF and BCNF. Normalizing data improves storage efficiency, data integrity, and scalability.
Students can learn Trees concept in data structures. various types of data structures like binary trees, expression trees, binary search trees and AVL trees are covered in this PPT.
Tabulation is the process of organizing statistical data into a table with rows and columns. There are three main types of tabulation: simple/one-way tabulation which organizes data by one characteristic; double/two-way tabulation which uses two characteristics; and complex tabulation which uses multiple characteristics. A statistical table includes components like the table number, title, captions, body, footnote and source.
This document discusses data structures and their uses. It defines data as collections of facts and figures that can be grouped or elementary. Entities contain attributes with values, and similar entities form entity sets. Data structures store and organize data in computer memory for efficient use. Linear data structures like arrays and linked lists arrange elements sequentially, while non-linear structures like trees and graphs show hierarchical or network relationships. Common linear structures are stacks and queues, and operations on data include traversing, searching, inserting, and deleting elements.
The document discusses database normalization through various normal forms including 1NF, 2NF, 3NF and BCNF. It provides examples of tables that violate different normal forms and how to convert them into the appropriate normal form by removing data redundancies and anomalies through decomposition. The goal of normalization is to organize data to avoid issues with data integrity like insertion, deletion and update anomalies.
The document discusses database normalization. It defines normalization as a process that makes data structures efficient by eliminating redundancy and inconsistencies. The key goals of normalization are to control redundancy, ensure data consistency, and allow complex queries. The document outlines the various normal forms including 1NF, 2NF, 3NF, BCNF and examples of how to normalize tables to each form by removing functional dependencies on non-key attributes.
Entity relationship diagram - Concept on normalizationSatya Pal
The document discusses database normalization from the entity relationship diagram stage through fifth normal form. It describes how entities from the ER diagram become tables and how relationships are modeled. Anomalies in unnormalized relations are explained, along with how different normal forms address these issues. The document also discusses denormalization techniques used to improve query performance and some limitations of normalization.
The document discusses database normalization. Normalization is the process of organizing data to avoid data redundancy and inconsistencies. It discusses the three normal forms - 1st normal form requires each table column contain atomic values, 2nd normal form requires columns depend on the whole primary key, and 3rd normal form removes transitive dependencies. The document also contrasts top-down design, which identifies entity types before attributes, versus bottom-up design, which groups attributes into entities.
This document summarizes the three types of internal tables in ABAP: standard tables, sorted tables, and hashed tables. Standard tables allow for index and key access and maintain row numbers internally. Sorted tables sort data by key and also allow for index and key access. Hashed tables only allow for key access and optimize access time for large tables by distributing data randomly using a hash function on the table key. The document describes the appropriate usage of each table type based on access needs and performance considerations.
This document discusses nonlinear data structures like trees and graphs. It defines trees and graphs, noting that all trees are graphs but not all graphs are trees. It covers topics like binary trees, binary search trees, tree traversal methods, graphs, spanning trees, and minimum spanning trees. Recursion is described as useful for processing trees by keeping track of what has been processed and what remains.
The document provides an overview of structured data presentation tools for digital humanities scholars. It discusses the difference between data presentation and analysis, and highlights some early pioneers of data visualization like William Playfair and Charles Minard. The document then examines challenges in using visualization for the humanities. It also profiles several structured data presentation tools, including TimeFlow, Google Fusion Tables, Many Eyes, and Omeka. Hands-on examples are provided using the Exhibit framework to create interactive visualizations like faceted browsing, searching, tables, timelines, and maps.
Kiehl's is a 151-year-old American skincare brand known for using natural ingredients. It was founded as an herbal pharmacy and acquired by L'Oreal in 2000. Kiehl's relies on word-of-mouth marketing and sampling rather than traditional ads. A new print ad will feature Sean Connery endorsing Kiehl's best-selling anti-aging serum to target men aged 30-60 who are concerned with reversing signs of aging. The simple black and white ad aims to enhance Kiehl's image of luxury and prestige.
This document discusses new media advertising strategies for a Korean cosmetics brand. It analyzes the brand's social media performance in 2013, finding that celebrity recommendations and promotional events attracted more attention than general news posts. Competitor analysis shows one brand has more social media "Likes" and engagement through varied interactive events. A SWOT analysis identifies the brand's positive perception but low awareness compared to competitors. The proposal recommends using owned and earned media more, differentiating the brand's natural philosophy through participatory events, and improving communication to build engagement.
How Olay India Used Twitter & Facebook for Sampling ActivityWATConsult
This case study documents a social media campaign by Olay India to increase their Twitter followers and engagement by offering free samples of their products. They promoted giving away free samples on Twitter and drove followers to their rewards website for the samples. This led to a significant increase in their Twitter followers from 816 to over 1780 and higher user engagement through mentions and outreach on the platform.
This document summarizes a proposal to improve fault tolerance in Hadoop clusters. It proposes adding a "Backup" state to store intermediate MapReduce data, so reducers can continue working even if mappers fail. It also proposes a "supernode" protocol where neighboring slave nodes communicate task information. If one node fails, a neighbor can take over its tasks without involving the JobTracker. This would improve fault tolerance by allowing computation to continue locally between nodes after failures.
M.A.C. was founded in 1985 in Toronto as a professional makeup brand. It focused on high quality products that could withstand stage lighting and long wear for models, actors, and performers. The brand developed a unique image through black clothing, extreme employee styles, and counters designed for professional artists. M.A.C. now sells products through partner retailers, company stores, and a 1-800 call center across 15 countries. The target consumer is both professional artists and younger fashion-forward retail customers seeking unique looks.
MAC Cosmetics was founded in 1984 in Toronto, Canada to create professional-quality makeup for photographers and makeup artists. It has since expanded to sell products worldwide to consumers directly. The company's mission is to be the leading authority in makeup and meet customers' needs. MAC practices corporate social responsibility through various initiatives that support communities affected by HIV/AIDS. Though MAC targets women, its products were initially made for makeup professionals.
An immersive workshop at General Assembly, SF. I typically teach this workshop at General Assembly, San Francisco. To see a list of my upcoming classes, visit https://generalassemb.ly/instructors/seth-familian/4813
I also teach this workshop as a private lunch-and-learn or half-day immersive session for corporate clients. To learn more about pricing and availability, please contact me at http://familian1.com
This chapter discusses database design and management. It describes relational and object-oriented database models. For relational databases, entities are represented as tables related by primary and foreign keys. Object databases represent data as objects with attributes and relationships. Hybrid systems store objects in relational databases. The chapter also covers distributed databases which partition data across multiple physical locations for performance and availability.
The document discusses various database models including flat file, hierarchical, network, relational, object-relational, and object-based models. It provides a brief history of database development, from manual files to relational databases. It describes key aspects of relational databases including how data is organized into logical tables with rows and columns.
This document provides an overview of database normalization concepts. It begins by defining normalization as the process of organizing data in a database to eliminate redundant data and ensure data dependencies are properly represented by constraints. It then discusses first normal form (1NF), which requires each cell to contain a single value. Candidate keys and super keys are also defined. The document concludes by briefly mentioning higher normal forms up to fifth normal form (5NF) and some alternative database design approaches such as NoSQL and graph databases.
good practices in formatting ms excel spreadsheetsDescartes Arreza
This document discusses good practices for formatting Excel spreadsheets used for research data. It describes including a data definition sheet with variable names and definitions. The data sheet then contains the column headers matching the variable names and data values. Formatting guidelines are provided, such as centering and bolding lowercase column headers, right aligning dates and numbers, and left aligning text. Consistent formatting across sheets is also recommended, including column spacing, fonts, and removing grid lines. An exercise is included to practice applying these guidelines.
This document provides an overview of databases and SQL. It discusses how data is organized in databases using tables, records, and fields. It then covers designing a relational database by creating entity relationship diagrams (ERDs) which show entities, attributes, and relationships. The document outlines the process of normalizing a database to remove duplication. It also introduces SQL for manipulating data by defining concepts like selecting, updating, deleting, and inserting data using commands targeted at specific tables and rows.
This document discusses key concepts related to databases including:
- Data hierarchy refers to the organization of data in a database with tables containing records made up of individual fields.
- A contact list on a mobile phone is an example of a simple database with tables (contacts), records (individual contacts) and fields (name, phone number, etc.).
- Primary keys uniquely identify each record in a table while foreign keys in one table match the primary key of another table to link the tables together in a relational database.
The document provides an overview of databases and database design. It defines what a database is, what databases do, and the components of database systems and applications. It discusses the database design process, including identifying fields, tables, keys, and relationships between tables. The document also covers database modeling techniques, normalization to eliminate redundant or inefficient data storage, and functional dependencies as constraints on attribute values.
The document provides an overview of the relational model for databases. The key points are:
- The relational model represents data in two-dimensional tables and organizes data into relational tables, presenting a logical view to users.
- Relational tables have properties like atomic values, unique rows, and insignificant column/row order. Relationships between tables are represented through primary and foreign keys.
- The relational model introduces concepts like normalization, relationships, keys, and operations that can be performed on relational tables and sets.
This document introduces several common data structures used in computer science, including arrays, linked lists, stacks, queues, trees, and graphs. Arrays store a collection of elements of the same type in a linear order. Linked lists consist of nodes that contain data and links to other nodes, allowing efficient insertion and removal. Stacks and queues are linear data structures where elements can only be added or removed from one end, with stacks following last-in first-out order and queues following first-in first-out order. Trees store hierarchical relationships between elements, and graphs represent relationships between elements without a defined hierarchy.
This document discusses different topics related to spatial data models and GIS database structures. It covers relational and object-oriented database structures, spatial data models including raster and vector models, common file formats like shapefile and geodatabase, as well as topics like data compression, topology, and relationships between spatial objects. Database normalization techniques are also explained to reduce redundancy and improve database design.
Database Structures – Relational, Object Oriented – ER diagram - spatial data models – Raster Data Structures – Raster Data Compression - Vector Data Structures - Raster vs Vector Models TIN and GRID data models - OGC standards - Data Quality.
The document discusses database structures and spatial data models used in geographic information systems. It covers relational and object-oriented database structures and describes features, entities, attributes, and relationships. Specifically, it discusses how GIS uses relational databases to organize spatial vector data into tables, features, and relationships between entities. It also describes object-oriented models and how they represent real-world objects and relationships.
This document provides information about database management systems (DBMS) and relational database management systems (RDBMS). It defines key concepts like data, information, tables, records, fields, primary keys, foreign keys and relationships. It also describes how to create and manage databases using MS Access. Functions like queries, forms, reports and SQL are explained. Different data types, creating and manipulating tables, inserting, updating and deleting records are covered.
An extended database reverse engineering v a key for database forensic invest...eSAT Journals
Abstract The database forensic investigation plays an important role in the field of computer. The data stored in the database is generally stored in the form of tables. However, it is difficult to extract meaningful data without blueprints of database because the table inside the database has exceedingly complicated relation and the role of the table and field in the table are ambiguous. Proving a computer crime require very complicated processes which are based on digital evidence collection, forensic analysis and investigation process. Current database reverse engineering researches presume that the information regarding semantics of attributes, primary keys, and foreign keys in database tables is complete. However, this may not be the case. Because in a recent database reverse engineering effort to derive a data model from a table-based database system, we find the data content of many attributes are not related to their names at all. Hence database reverse engineering researches is used to extracts the information regarding semantics of attributes, primary keys, and foreign keys, different consistency constraints in database tables. In this paper, different database reverse engineering (DBRE) process such as table relationship analysis and entity relationship analysis are described .We can extracts an extended entity-relationship diagram from a table-based database with little descriptions for the fields in its tables and no description for keys. Also the analysis of the table relationship using database system catalogue, joins of tables, and design of the process extraction for examination of data is described. Data extraction methods will be used for the digital forensics, which more easily acquires digital evidences from databases using table relationship, entity relationship, different joins among the tables etc. By acquiring these techniques it will be possible for the database user to detect database tampering and dishonest manipulation of database. Index Terms: – Foreign key; Table Relationship; DB Forensic; DBRE;
An extended database reverse engineering – a key for database forensic invest...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
LP&IIS2013.Chinese Named Entity Recognition with Conditional Random Fields in...Lifeng (Aaron) Han
Authors: Aaron Li-Feng Han, Derek Fai Wong and Lidia Sam Chao
In Proceeding of International Conference of Language Processing and Intelligent Information Systems. M.A. Klopotek et al. (Eds.): IIS 2013, LNCS Vol. 7912, pp. 57–68, 17 - 18 June 2013, Warsaw, Poland. Springer-Verlag Berlin Heidelberg 2013
This document provides an overview of MS Access and database design. It discusses key concepts like relational databases, tables, records, and fields. It also outlines the steps to create tables and define fields, add additional tables, create queries, forms and reports, and use templates to design a database in MS Access. The goal is to organize data without duplication and ensure consistency through techniques like normalization.
This document provides an overview of how tables are structured and styled in DrawingML. It explains that table styles define the visual presentation of the table separately from the data. It then describes the individual style components that make up a table style including the table background, whole table properties, and style option properties. It also summarizes how table data is defined and structured within a table including table properties, rows, grids, and cells.
This document discusses different data structures, including their definitions, classifications, and common operations. It defines a data structure as a way to organize data by considering the elements stored and their relationships. Data structures covered include arrays, linked lists, stacks, queues, trees, graphs, and hash tables. They are classified as linear (processed sequentially) versus non-linear, and primitive (direct machine representation) versus non-primitive. Common operations on data structures are traversing, searching, inserting, and deleting.
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Methodology and software for extracting and transforming data from arbitrary ...Alexey Shigarov
Methodology and software for extracting and transforming data from arbitrary tables in untagged PDF-documents to the relational form (flat file databases) (in Russian)
CRL: A Rule Language for Table Analysis and InterpretationAlexey Shigarov
Tables presented in spreadsheets can be a source of important information that needs to be loaded into relational databases. However, many of them have complex structures. This does not allow to populate databases with their information directly. The presentation is devoted to the issues of the rule-based information extraction from arbitrary tables presented in spreadsheets and its transformation into structured canonical form that can be loaded into a database by standard ETL tools. We suggest a novel rule language called CRL for table analysis and interpretation. It enables developing a simple program to recover missing relationships describing table semantics. Particular sets of rules can be designed for different types of tables to provide extraction and transformation steps in a process of unstructured tabular data integration.
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The presentation shows PhD study, the technology for tabular information extraction from documents in various formats printed as Enhanced metafiles. You can find more information at http://cells.icc.ru
System for tabular information extraction from documents in various formatsAlexey Shigarov
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The X‐Pattern Merging of the Equatorial IonizationAnomaly Crests During Geoma...Sérgio Sacani
A unique phenomenon—A geomagnetically quiet time merging of Equatorial IonizationAnomaly (EIA) crests, leading to an X‐pattern (EIA‐X) around the magnetic equator—has been observed in thenight‐time ionospheric measurements by the Global‐scale Observations of the Limb and Disk mission. Thepattern is also reproduced in an ionospheric model that assimilates slant Total Electron Content from GlobalNavigation Satellite System and Constellation Observing System for Meteorology, Ionosphere, and Climate 2.A free‐running whole atmospheric general circulation model simulation reproduces a similar pattern. Due to thesimilarity between measurements and simulations, the latter is used to diagnose this heretofore unexplainedphenomenon. The simulation shows that the EIA‐X can occur during geomagnetically quiet conditions and inthe afternoon to evening sector at a longitude where the vertical drift is downward. The downward vertical driftis a necessary but not sufficient condition. The simulation was performed under constant low‐solar andquiescent‐geomagnetic forcing conditions, therefore we conclude that EIA‐X can be driven by lower‐atmospheric forcing.
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The Dahlgren, Thorne, and Stebbins system of classification is a modern method for categorizing angiosperms (flowering plants) based on phylogenetic relationships. Developed by botanists Rolf Dahlgren, Robert Thorne, and G. Ledyard Stebbins, this system emphasizes evolutionary relationships and incorporates extensive morphological and molecular data. It aims to provide a more accurate reflection of the genetic and evolutionary connections among angiosperm families and orders, facilitating a better understanding of plant diversity and evolution. This classification system is a valuable tool for botanists, researchers, and horticulturists in studying and organizing the vast diversity of flowering plants.
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This an presentation about electrostatic force. This topic is from class 8 Force and Pressure lesson from ncert . I think this might be helpful for you. In this presentation there are 4 content they are Introduction, types, examples and demonstration. The demonstration should be done by yourself
A slightly oblate dark matter halo revealed by a retrograde precessing Galact...Sérgio Sacani
The shape of the dark matter (DM) halo is key to understanding the
hierarchical formation of the Galaxy. Despite extensive eforts in recent
decades, however, its shape remains a matter of debate, with suggestions
ranging from strongly oblate to prolate. Here, we present a new constraint
on its present shape by directly measuring the evolution of the Galactic
disk warp with time, as traced by accurate distance estimates and precise
age determinations for about 2,600 classical Cepheids. We show that the
Galactic warp is mildly precessing in a retrograde direction at a rate of
ω = −2.1 ± 0.5 (statistical) ± 0.6 (systematic) km s−1 kpc−1 for the outer disk
over the Galactocentric radius [7.5, 25] kpc, decreasing with radius. This
constrains the shape of the DM halo to be slightly oblate with a fattening
(minor axis to major axis ratio) in the range 0.84 ≤ qΦ ≤ 0.96. Given the
young nature of the disk warp traced by Cepheids (less than 200 Myr), our
approach directly measures the shape of the present-day DM halo. This
measurement, combined with other measurements from older tracers,
could provide vital constraints on the evolution of the DM halo and the
assembly history of the Galaxy.
The extremotolerant desert moss Syntrichia caninervis is a promising pioneer ...Sérgio Sacani
Many plans to establish human settlements on other planets focus on
adapting crops to growth in controlled environments. However, these settlements will also require pioneer plants that can grow in the soils and
harsh conditions found in extraterrestrial environments, such as those
on Mars. Here, we report the extraordinary environmental resilience of Syntrichia caninervis, a desert moss that thrives in various extreme environments. S. caninervis has remarkable desiccation tolerance; even after
losing >98% of its cellular water content, it can recover photosynthetic
and physiological activities within seconds after rehydration. Intact plants
can tolerate ultra-low temperatures and regenerate even after being stored
in a freezer at 80C for 5 years or in liquid nitrogen for 1 month.
S. caninervis also has super-resistance to gamma irradiation and can survive and maintain vitality in simulated Mars conditions; i.e., when simultaneously exposed to an anoxic atmosphere, extreme desiccation, low temperatures, and intense UV radiation. Our study shows that S. caninervis is
among the most stress tolerant organisms. This work provides fundamental insights into the multi-stress tolerance of the desert moss
S. caninervis, a promising candidate pioneer plant for colonizing extraterrestrial environments, laying the foundation for building biologically sustainable human habitats beyond Earth.
PART 1 The New Natural Principles of Electromagnetism and Electromagnetic Fie...Thane Heins
Document Summary and the History of Perpetual Motion
Every single Faraday Generator coil since 1834 has been and is currently performing Negative Work at infinite efficiency with created Electromagnetic Field Energy during electricity generation and its physical Kinetic Energy reduction or Electromagnetic Resistance of the changing magnetic field which is initially inducing Electric Current in the generator coil according to Faraday's Law of Induction.
The Work-Energy Principle confirms mathematically that the magnitude of the changing magnetic field's Kinetic Energy reduction is equal to the magnitude of Negative Work performed at infinite efficiency, which is equal to the magnitude of Energy (Electromagnetic Field Energy which is created according to Oersted's Law of Creation of Energy of 1820). Created Electromagnetic Field Energy is required in order to perform the Negative Work – because Work cannot be performed in the absence of Energy.
In 2007 Thane Heins of Almonte Ontario, Canada discovered that unlimited amounts of Positive Electromechanical Work could be performed at infinite efficiency with created and TIME DELAYED Electromagnetic Field Energy.
Every single ReGenX Generator coil since 2007 has been and is currently performing Positive Work at infinite efficiency with created Electromagnetic Field Energy during electricity generation and during its physical Kinetic Energy increase or Electromagnetic Assistance of the changing magnetic field which is initially inducing Electric Current in the generator coil according to Heins' Law of Induction.
Faraday Electric Generators all harness internally Created Electromagnetic Field Energy in order to perform Negative Work (system Kinetic Energy reduction) at infinite efficiency and ReGenX Electric Generators harness internally created and Time Delayed Electromagnetic Field Energy in order to perform Positive Work (system Kinetic Energy increase) at infinite efficiency.
Both Faraday Generators and ReGenX Generators operate as Perpetual Motion Machines of the First Kind because they both have the ability to perform both Negative or Positive Work indefinitely and at infinite efficiency without requiring any External Energy input. The unlimited Energy required to perform either the Negative or Positive Work is created at the Sub-Atomic Quantum Electron level inside the generators' Current Bearing Wires according to the Law of Creation of Energy.
Hans Christian Oersted discovered the Law of Creation of Energy in 1820 when he demonstrated the world's first Perpetual Motion Machine of the First Kind at the University of Copenhagen when he also simultaneously violated Newton's 1st, 2nd and 3rd Laws of Motion.
Michael Faraday built and demonstrated the world's second Perpetual Motion Machine of the First Kind in 1822 when he demonstrated his Electric Motor invention which harnessed created Electromagnetic Field Energy in order to perform Positive Electromechanical Work at infinite efficienc
A mature quasar at cosmic dawn revealed by JWST rest-frame infrared spectroscopySérgio Sacani
The rapid assembly of the first supermassive black holes is an enduring mystery. Until now, it was not known whether quasar ‘feeding’ structures (the ‘hot torus’) could assemble as fast as the smaller-scale quasar structures. We present JWST/MRS (rest-frame infrared) spectroscopic observations of the quasar J1120+0641 at z = 7.0848 (well within the epoch of reionization). The hot torus dust was clearly detected at λrest ≃ 1.3 μm, with a black-body temperature of
K, slightly elevated compared to similarly luminous quasars at lower redshifts. Importantly, the supermassive black hole mass of J1120+0641 based on the Hα line (accessible only with JWST), MBH = 1.52 ± 0.17 × 109 M⊙, is in good agreement with previous ground-based rest-frame ultraviolet Mg II measurements. Comparing the ratios of the Hα, Paα and Paβ emission lines to predictions from a simple one-phase Cloudy model, we find that they are consistent with originating from a common broad-line region with physical parameters that are consistent with lower-redshift quasars. Together, this implies that J1120+0641’s accretion structures must have assembled very quickly, as they appear fully ‘mature’ less than 760 Myr after the Big Bang.
From Unstructured to Structured Tabular Data Using a Rule Engine
1. A Journey of Tabular Information
from Unstructured to Structured Data World
Using a Rule Engine*
Alexey Shigarov1
shigarov@icc.ru
1 Institute for System Dynamics and Control Theory
of the Siberian Branch of the Russian Academy of Sciences
HARBIN
October 18, 2014
* The work was partially supported by the Russian Foundation for Basic Research
(Grant No 14-07-00166) and the Council for grants of the President of the Russian Federation
(Scholarship No SP-3387.2013.5)
2. UNSTRUCTURED
INFORMATION1,2
Scientific Papers
Financial Reports
E-mail Messages
Images, PDF, ASCII-text
Word Documents
Excel Workbooks
HTML Pages
STRUCTURED
INFORMATION1,2
has predefined
formal data model
(e.g. relational
databases)
What’s the problem with using data?
It’s interpreted
by human
?
1 Inmon W. Matching unstructured data and structured data // The data
administration newsletter. 2006. http://www.tdan.com/view-articles/5009
2 Blumberg R., Atre S. The problem with unstructured data // DM Review,
2003. http://soquelgroup.com/Articles/dmreview_0203_problem.pdf 2
It’s interpreted
by computer
How to journey from unstructured
to structured data world
3. UNSTRUCTURED
TABULAR DATA
Tables in scanned
documents, ASCII-text,
PDF, Word Processors,
Spreadsheets, and HTML
formats
STRUCTURED DATA
Databases,
Data Warehouses
How to convert tabular data from
unstructured to structured form?
?
3
One of the most important issues is
You can do it here
Structured Queries
Data Mining
Knowledge Discovery
Online Analytical Processing
Question Answering
etc.
You can do it here
Reading, Writing, and Editing Tables
4. A bit about terminology
Unstructured textual data
Weakly-structured documents1
contain typographical indicators of structures to
identify headers, paragraphs, lists, tables, etc.
(ASCII-text, PDF, etc.)
Semi-structured documents1
have metadata
to describe their structures
(HTML, Word, Excel)
Analogically we can define
Weakly-structured tables
contain typographical indicators of structures
(indents, spaces, ASCII-art, lines, etc.)
(ASCII-text, PDF, etc.)
Analogically we can define
Semi-structured tables
have metadata to describe
their location, columns and rows
(HTML, Word, Excel)
1 The terms from the paper Feldman R., Sanger J. The Text Mining
Handbook: Advanced Approaches in Analyzing Unstructured
Data // Cambridge University Press. 2006. 422 p.
Analogically we can define
Unstructured tabular data
4
5. Names for the conversion form unstructured to structured
tabular data
• Table Understanding1
includes the following subtasks: (1) table location, (2) table recognition, (3) functional and (4) structural
analysis, and (5) table interpretation
1 Hurst M. Layout and Language: Challenges for Table Understanding on the Web // In Proc. of the 1st
Int. Workshop on Web Document Analysis. 2001. pp. 27-30.
• Table Understanding2
consists in the recovering of relationships between labels (headers) and data values as well as between labels
and dimensions (domains)
• Information Extraction from Tables2
“Information extraction from tables is perhaps analogous to the task of the same name applied to sentential
text. The narrow definition requires a target schema and requires that arbitrary input (generally of some
standard encoding) be transformed into instances of the schema” 2
2 Embley D.W., Hurst M., Lopresti D., Nagy G. Table-Processing Paradigms: a Research Survey // Int. J. on
Document Analysis and Recognition. 2006. Vol. 8, No. 2. pp. 66-86.
• Table Canonicalization3
is transformation of a table to the canonical form that fits into the table of relational database
3 Tijerino Y., Embley D., Lonsdale D., Nagy G. Towards Ontology Generation from Tables // World Wide
Web: Internet and Web Information Systems. 2005. Vol. 8, No 3. pp. 261-285.
5
6. The way from unstructured to structured tabular data
as the recovering of unknown information
Table Location*
to recover the
bounding box
of a table
6
* The terms from the paper Hurst M. Layout and Language: Challenges for Table
Understanding on the Web // In Proc. Of the Int. Workshop on Web Document Analysis.
2001. pp. 27-30.
Table Extraction
Information Extraction from Tables
Table Recognition*
to recover columns,
rows, and cells
Source: a table in a weakly
or semi-structured document
Target: a table in the canonical form
(which fits to a relational database
Functional and Structural Analysis,
and Interpretation of a Table*
to recover semantic relationships
(including cell-role, label-value, label-
label, label-dimension pairs)
Semantic Relationship Reconstruction
7. Answer to question how to extract information
from tables depends on how they are presented
7
High-Level
Format What we have initially
What we need
to extract data
Excel Workbooks
Word Documents
HTML Pages
Cells with their
positions, styles, and
content (text, images) Semantic Relationship
Reconstruction (Functional
and Structural Analysis,
and Interpretation)
Table Extraction
(Location and Recognition)
PDF Documents
Characters with their
positions, font metrics,
as well as graphics
Plain-Text
Characters with their
positions
Scanned
Documents
Bitmaps
Character Recognition
And so on
Low-Level
Structured Data
8. State-of-the-art in Information Extraction from Tables
8
Tasks & Formats Research & Development, Software Area
Semantic
Relationship
Reconstruction
Douglas S. (1995), Hurst M. (2000), e Silva A.C. (2004), Embley D.
(2005), Tijerino Y. (2005), Pivk A. (2006), Gatterbauer W. (2007), Tao C.
(2009) et al.
Information
ExtractionTable Location and
Recognition in
HTML
Chen H.-H. (2000), Hurst M. (2001) , Yoshida M. (2001), Cohen W.W.
(2002), Wang Y. (2002), Lerman K. (2004), Tengli A. (2004), Embley D.W.
(2005), Tijerino Y. (2005), Krüpl B. (2006), Gatterbauer W. (2006, 2007),
Weizsäcker L. (2008), et al.
Table Location and
Recognition in
PDF, PS, or EMF
Ramel J.-Y. (2003), Hassan T. (2007), Hirano T. (2007) , Liu Y. (2007),
Shigarov A. (2009, 2010), et al., PDF to Word/Excel Converters
Document
Analysis and
Recognition
Table Location and
Recognition in
Plain-text
Rus D. (1994), Douglas S. (1995), Tupaj S. (1996), Pyreddy P. (1997),
Hurst M. (1997, 2001, 2003), Kieninger T. (1998, 1999, 2001), Ng H.T.
(1999) , Hu J. (2000, 2001), Klein B. (2001), Pinto D. (2003) , e Silva A.C.
(2004), Li J. (2006), et al.
Table Location and
Recognition in
Scanned
Documents
Kojima H. (1990), Chandran S. (1993), Itonori K. (1993), Green E.
(1995), Hirayama Y. (1995), Watanabe T. (1995), Zuev K. (1997),
Shamillian J.H. (1997), Tersteegen W.T. (1998), Handley J.C. (1999),
Cesarini F. (2002), Wang Y. (2002), Wasserman H. (2002) , Mandal S.
(2004, 2006), et al., OCR Systems
Character
Recognition
OCR Systems
9. Challenges in table understanding
• A huge amount of ways to portray a table
o Features originate from typographical standards, corporative practice, ad hoc software,
data formats, and human inventiveness
• Assumptions about tables serve to reduce the complexity of table understanding
o Usually those assumptions are entirely embedded in algorithms of existed systems
o It constrains a range of tables which are successfully understood by these systems
• Today, no recognized corpus of test tables to evaluate a table understanding
system
9
10. 10
Output structured tabular data
Input unstructured tabular data
Our purpose
is to develop software for the conversion of tabular data from unstructured
sources (like Excel workbooks, Word documents, HTML pages) to databases
11. Our approach to the semantic relationship
reconstruction
11
• We assume that
o Tables produced by the same vendor often have similar layout, formatting, and content
o It allows to define a template describing a class of these tables or how they can be interpreted
• We propose
o To use a set of formalized rules (a knowledge base) for recovering semantic relationships
(i.e. cell-role, label-value, label-label, label-dimension pairs) in a table from the class
o The rules define how we can interpret what we know (i.e. positions, style settings, and content of
cells) to recover what we don’t know (i.e. semantic relationships)
• It is expected that
o Implementation of rule sets for different table classes provides processing of a wide range of tables
having various structures and features
12. What we mean when we say “Table”
Table structure on the bottom level
12
A B C
A cell can include
multiple tiles in Excel,
Word, HTML, or LaTeX
A cell can visually include
a few tiles for human
reading using graphical
lines
Perhaps nobody presents
a cell like this
• Cell positions (row and column coordinates)
• Merged cells (as shown in Figure A, but not in Figure B or C)
• Cell style (border style, content placement, text metrics, etc.)
• Cell content (a text and/or images, but not other tables or cells)
Perhaps, it’s a table
generated in Excel,
Word, HTML, etc.
13. What we mean when we say “Table”
Table structure on the top level
13
• A cell serves as either entry* or label*
• An entry represents data value
• A label describes (addresses) entries
• A label can address entries and other labels
either in rows or in columns where it spans them
• A label can be a value of a dimension
* The terms “entry” and “label”
correspond to the meaning that was
suggested in the paper Wang X.
Tabular Abstraction, Editing, and
Formatting. PhD Thesis. Waterloo,
Ontario, Canada. 1996.
Perhaps, it’s a pivot table
generated by an OLAP system
14. CELLS table model, Bottom level
Known facts about a table
Tb = ( Sr, Sc, C ), where
Sr — a set of rows
Sc — a set of columns
С — a set of cells
A cell — с = ( p, S, c' ), where
p = ( cl, rt, cr, rb ) — positions in the row-
column coordinate system (Sr and Sc sets)
S — style settings (including colors, font
metrics, adjustment, styles of borders etc.)
c' — a content (text, images)
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15. CELLS table model, Top level
Unknown facts about a table
Tt = ( D, Lr, Lc, E ), where
D — a set of domains
Lr — a tree of row labels
Lc — a tree of column labels
E — a set of entries
An entry — e = ( D', L', e' ), where
D' — a subset of values of domains from D related with this entry
L' — a set of labels from trees Lr and Lc related with this entry
e' — a content
A label — l = ( l' ), where
l' — a content, which is not a value
of any domain from D
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16. Proposed schema for the rule-based extraction information from tables
Table Extraction
16
Domain
Fact Base 1
Domain
Fact Base 2
Domain Facts
CELLS Table Model
Bottom Level
CELLS Table Model
Top Level
Target: a table in the canonical form
(which fits to a relational database)
Class 1
Table Sources
Class 2
Class 3
Canonical Table
GenerationSource: a table in a weakly
or semi-structured document
Table
Knowledge
Generation
Domain
Knowledge
Generation
Knowledge
Base 1
Table Interpretation Rules
Knowledge
Base 2
Semantic
Relationship
Reconstruction
using a rule
engine
17. Known facts about domains
Table interpretation rules
CELLS Table model
Bottom level
Known facts:
cell positions, style
settings, and a content,
as well as domains
CELLS Table model
Top level
Unknown facts:
semantic relationships
(cell-role, label-entry,
label-label, and label-
domain pairs)
17
The right hand side (then) of a rule
recovers unknown facts about a table,
including assignment cell roles (label or entry),
binding cells (i.e. creating label-entry, label-
label, and label-domain pairs),
etc.
The left hand side (when) of a rule
defines conditions using known facts
about cells and domains
Domain
Dictionaries
Known facts
about domains
18. Rules for table structure analysis*
Sample 1
...
when
$c : CCell( cl == 1 )
then
modify ( $c ) { setRole( Role.ROWLABEL ) }
...
*The rules are written by the expression language MVEL http://mvel.codehaus.org
for the rule engine Drools Expert http://www.jboss.org/drools
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20. Rules for table structure analysis
Sample 3
...
when
$c : CCell( cl == 1, cl == cr, text matches "(?i).*(total)" )
then
modify ( $c ) { setIgnored( true ) }
...
…TOTAL
…Total
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21. Rules for table structure analysis
Sample 4
...
when
$l : CCell( role == Role.COLLABEL )
$e : CCell( role == Role.ENTRY, cl == $l.cl, cr == $l.cr )
then
$e.addConnectedCell( $l )
...
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22. Rules for table structure analysis
Sample 5
...
when
$d : CDimension( name == "Religion" )
$c : CCell ( cl == 1, rt > 1,
text != null, role == null,
style.getFont().getColor() == "#ff0000" )
not ( exists CCell ( cl > $c.cr, rt == $c.rt, text != null ) )
then
$c.setDimension( $d )
... Text Text Text
Text
Text Text Text Text
Text Text Text Text
Text Text Text Text
Text
Text Text Text Text
Text Text Text Text
22
More samples of rules are available
at address http://cells.icc.ru/test
23. Unknown border style settings of a cell are recovered by border style settings of its
neighbor cells (b)
It allows simplifying table interpretation rules
Optional preprocessing for cells
23
rightBorder = MEDIUM leftBorder = NONE
rightBorder = MEDIUM leftBorder = MEDIUM
• Elimination of empty rows and columns
• Cell border enhancement
Visual borders of a cell are not always its physical borders
They can be visually formed by borders of its neighbor cells (a)
a
b
24. Optional pre- and post-processing for text
• Removal of whitespaces and special symbols
For example, the expression ˽ ˽ ˽ ˽ Total....... is converted to Total
• Conversion from synonymous to reference expressions using reference dictionaries
For example, the following synonyms: 2014, FY2014, 2014 год, Current year
can have the same meaning Year 2014
A reference dictionary is a set of pairs (Rs, Rt), where
Rs — a source natural language or regular expression
Rt ― a target natural language or regular expression
For example, the pair (FY[2][0][0-1][0-4], [2][0][0-1][0-4]) allows converting
all synonyms sort of FY2000,…,FY2014 to the following reference expressions 2000,…,2014
correspondingly
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25. Optional post-processing for label trees
• Optionally, labels in trees can be assigned to domains using domain dictionaries
• A Domain Dictionary is a set of pairs (R, Di), where
R — a natural language or regular expression
Di — a domain
• In the result label trees can be reduced or completely degenerate
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D1 (OPERATION) = {Sent, Received}
D2 (YEAR) = {2010, 2011}
D3 (MAIL_TYPE) = {Letters, Parcels}
D1 (OPERATION) D2 (YEAR)
Sent
Letters
Parcels
2011
2010
Letters
Parcels
Received
Letters
Parcels
2011
2010
Letters
Parcels
D3 (MAIL_TYPE)
Sent
Letters
Parcels
Received
Letters
Parcels
D2 (YEAR) = {2010, 2011} After post-processing
in the perfect case
After post-processing
in the common case
Before post-processing
26. Generation of a table in the canonical form
• A generated table in the canonical form
consists of the following fields
DATA contains entries
ROW_LABEL contains paths from
leaves to roots in the non-degenerate
tree of row labels Lr
COL_LABEL contains paths from
leaves to roots in the non-degenerate
tree of column labels Lc
D_1,…,D_N present values of the
corresponding domains Di from the
set D
• Generated tables in the canonical form
can be exported into a relational database
using standard tools of database
management systems
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The instance of an output table
in the canonical form
27. Test data
$START
Company name
Place of
incorporation
and operation Activity
Percentage
held as of
December 31,
2006
Percentage
held as of
December 31,
2005
LLC “Airport Moscow” Moscow region Cargo handling 50,00% 50,00%
CJSC “Aerofirst” Moscow region Trading 33,30% 33,30%
CJSC “TZK
Sheremetyevo” Moscow region Fuel trading company 31,00% 31,00%
CJSC “AeroMASH – AB” Moscow region Aviation security 45,00% 45,00%
$END
We collected a corpus (~100 tables in Excel spreadsheet format)
They are available at address http://cells.icc.ru/test
Tag for identifying
the start point of a table
Tag for identifying
the end point of a table
• Any test table has precise layout of their cells
• Additionally we use special tags to locate a test table
in the corresponding Excel sheet
• It allows to avoid steps of table detection and table structure
recognition (i.e. identifying columns and rows)
27
28. Municipal Private Others
Forest land area (1,000 ha) 25 121 7 838 2 796 14 440 46
Forest growing stock (1 mil. m3) 4 040 1 011 433 2 590 5
Planted forests
Land area (1,000 ha) 10 361 2 411 1 232 6 705 12
Growing stock (1 mil. m3) 2 338 368 255 1 712 3
Natural forests
Land area (1,000 ha) 13 349 4 770 1 426 7 126 27
Growing stock (1 mil. m3) 1 701 642 178 878 3
Non-national forestNational
forestTotalItem
Value
(billion yen)
Annual increase
rate (%)
Value
(billion yen)
Annual increase
rate (%)
1990 339,4 3 371,9 12,7 0,91
1995 562,1 21,6 391,7 5,7 1,43
2000 1057,9 10,1 443,3 8 2,39
2002 1386,8 11,2 541,7 -1,2 2,56
2003 1512,2 9 563,8 4,1 2,68
2004 1769,4 17 567,6 0,7 3,12
2005 2028,3 14,6 703,7 24 2,88
Fiscal
year
Exports
value /
Imports
Value
Technology Trade
Exports Imports
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Sample of test tables
From Statistical Handbook of Japan 2007
31. # 高 速
Express
-way
# 一 级
First
Class
# 二 级
Second
Class
全 国 National Total 71897,5 121557 1765222 1382926 25130 27468 197143 382296
北 京 Beijing 1138,1 14359 13940 463 331 1822 419
天 津 Tianjin 681,6 443 9696 9126 331 404 1408 570
河 北 Hebei 4585,7 75 63079 53995 1591 2050 9835 9084
山 西 Shanxi 3050,5 305 59611 57250 1070 734 8851 2361
内蒙古 Inner Mongolia 6192,6 1188 72673 63000 252 330 6069 9673
辽 宁 Liaoning 3799,8 813 48051 47769 1637 987 10770 282
吉 林 Jilin 3561,8 1787 41095 38408 542 1120 4918 2687
黑龙江 Heilongjiang 5502,8 5057 63046 57882 413 707 5821 5164
上 海 Shanghai 256,5 2037 6286 6024 240 442 1203 262
江 苏 Jiangsu 1340,4 23899 60141 49959 1704 3085 10637 10182
浙 江 Zhejiang 1300,1 10408 45646 42759 1307 2070 5777 2887
安 徽 Anhui 2219,7 5611 67547 61406 866 300 7480 6141
福 建 Fujian 1453,9 3701 54155 41220 583 278 5573 12935
江 西 Jiangxi 2368,6 5537 60696 36070 666 314 6731 24626
山 东 Shandong 2855,4 2552 74029 73884 2411 3521 20251 145
地 区 Region
铁 路
营业里程
Length of
Railways
in
Operation
内河航
道里程
Length of
Navigable
Inland
Waterways
公 路
里 程
Total
Length of
Highways
等外路
Highway
Below
Class IV
等级路
Expressway
and Class
I to IV
Highway
Sample of a test table
From China Statistical Yearbook 2003
32. Experimental evaluation
32
Source
Number of Inference time
(ms) ***
Tables Cells Entries Labels
Relationships
(label-label)*
Rules
**
JAPAN_STAT1 15 1088 734 257 102 10 417
AEROFLOT2 13 2047 727 321 167 16 526
BOEING3 21 2156 964 470 196 14 663
CHINA_STAT4 18 7216 4180 862 551 12 964
CHEVRON5 7 812 268 141 89 12 283
USDA_NASS6 7 1553 1175 313 174 16 638
TOBACCO7 16 2844 2195 508 335 10 730
1Statistical Handbook of Japan 2007. Chapter 5, 8. Statistics Bureau of Japan.
2OJSC "Aeroflot – Russian Airlines" Consolidated Financial Statements For the Year Ended December 31, 2006. Aeroflot.
3Boeing Co, Annual Report 2010. Boeing Co.
4China statistical yearbook 2003
5Chevron Corp. News Release November 2, 2012
6Agricultural Statistics Annual. Chapter VI Statistics of hay, seeds, and minor field. USDA NASS. 2003
7Tobacco: World Markets and Trade 2005. USDA (U.S. Department of Agriculture). Foreign Agricultural Service
* Excluding relationships from roots of label trees
** Rules and results are available at http://cells.icc.ru/test
*** For the processor Intel Core 2 Quad, 2,66 ГГц and the rule engine Drools Expert (5.4.0.Final), http://www.jboss.org/drools
33. Related work
33
• Hurst M. The Interpretation of Tables in Texts. PhD. Thesis. School of Cognitive Science, Informatics, The
University of Edinburgh. UK, 2000.
• e Silva A.C., Jorge A.M., Torgo L. Design of an End-to-End Method to Extract Information from Tables //
Int. J. on Document Analysis and Recognition. Springer-Verlag. 2006. Vol. 8, No. 2. pp. 144–171.
• Embley D.W., Tao C., Liddle S.W. Automating the Extraction of Data from HTML Tables with Unknown
Structure // Data & Knowledge Engineering. Elsevier. 2005. Vol. 54, No 1, pp. 3–28.
• Tijerino Y., Embley D., Lonsdale D., Nagy G. Towards Ontology Generation from Tables // World Wide
Web: Internet and Web Information Systems. 2005. Vol. 8, No 3. pp. 261–285.
• Pivk A., Cimiano P., Sure Y., Gams M., Rajkoviˇc V., Studer R. Transforming Arbitrary Tables into Logical
Form with TARTAR // Data & Knowledge Engineering. 2007. Vol. 60 , pp. 567–595.
• Gatterbauer W., Bohunsky P., Herzog M., Krüpl B., Pollak B. Towards Domain-Independent Information
Extraction from Web Tables // In Proc. of the 16th Int. Conf. on World Wide Web. ACM New York, NY, US,
2007. pp. 71–80.
34. Contribution
34
• Implementation of the rule-based approach to table understanding
• Using both domain independent (spatial and style) information and domain-
specific (natural-language) information for table analysis and interpretation
• Dealing with table features like cut-ins (headers in a table body), non-numerical
data values, the duplication of multilingual labels, label columns which are
alternated by data columns, and so on
Application
• Unstructured tabular data integration in business intelligence
• Populating databases with statistical information
• Information extraction from financial reports
35. Conclusion
• Our approach is oriented
1. to use in table-processing all or nearly all of tabular data available in a source
(spatial structure, styles, and natural language)
2. to be applied to conversion of tabular data from unstructured to structured form as
part of information integration
• Now, our system provides information extraction from a wide range of tables in Excel
spreadsheet format
• Perhaps, further development of the proposed model, data structures, as well as post-
processing and preprocessing algorithms allows to simplify the writing of rules
• Each original class of tables produced by the same vendor potentially requires developing
a suitable knowledge base
• Perhaps, development of an unified knowledge base for heterogeneous sources from
various vendors is too expensive or even impossible since they often are contradictory
35
36. Collaboration
• If you interested in using our technologies for your tasks of large-volume
conversion of tabular data from unstructured sources to databases
• If you interested in a cooperative research project
• Please, e-mail us at shigarov@icc.ru
• We are interested in the development and use of our technologies both in
research and practice
36