UKeScience05-AnnSciData-Bose

"Annotating scientific data:why it is important and why it is difficult", Rajendra Bose, peter Buneman, Denise Ecklund

Some existing annotation systems:

• Swiss-Prot: annotation database produced by specialist curators
• TrEMBL: automated annotations for proteins ( link )
• IBMdeveloperWorks: an annotation is an XML document that is linked to a target data object

Problems and solutions:

• "pointers" to entries in other databases are sometimes used in the fields to provide mappings between co-ordinate systems
• it's easy to propagate the annotations through the operations of the relational algebra BUT inverting these rules is non-deterministic because the annotation in the output could have come from more than one place in the input
• An extension of SQL can be developed to propagate the annotations. One problem is when one attribute from two different tables, with two different annotations in each table, is part of the WHERE clause but not from SELECT clause. In this case, should both annotations be sent ? The solution can be to allow user to control the flow of annotation by adding some further propagation instructions to the SQL query
  
• For reasons of database security many annotations are stored externally. They require a coordinate system in order to specify how they are to be attached to the data
• External annotations can be retrieve by given the tuple: (table_name, tuple_id, the attribute_name) -- this can be a stable coordinate system

Open problems:

• Many existing annotation system provide only a limited ability to query over annotation values
• One needs to know where the annotation is attached to the base data and perhaps why it is attached. How can this be captured in the database and expressed in the query ?
• Understanding the movement of annotations in the manually curated databases
  

VLDBJ 05 -Annotations on Relational DBS

"An Annotation Management System for Relational Databases", Deepavali Bhagwat, Laura Chiticariu, Wang-Chiew Tan, Gaurav Vijayvargiya

• Introduces a system to annotate the source data. They store annotations in a special attribute
  
• Describe 3 methods to propagate the annotations, motivated by different needs:
• • custom
  • default scheme: if the output data is copied then the annotation is propagated
  • default-all scheme: propagate annotations according to where the data is copied from in ALL equivalent formulations of the given query
• a new SQL language is introduced (pSQL). This extension supports PROPAGATE clause which allow a user to specify how annotations should be propagate. Does not support aggregates and bag semantics (i.e. DISTINCT keyword must be present)
  

vldb09-BelieveAnn-Suciu

"Believe It or Not: Adding belief Annotations to Databases", W. Gatterbauer, M.Balazinska, N.Khoussainova, D.Suciu

• Define a belief database as a set of belief statements. It contains base information in the form of ground tuples, annotated with belief statements
  
• Describe a model of database annotations that allow users to annotate both the content and other users' annotations with belief
  
• Use of a concrete semantics to annotations that helps users engage in a structured discussion on content and each other's annotations.
  
• Assume, by default, that a user believes every belief statement that is in the database, unless stated otherwise
• The annotations are of the boolean logical form: "Bob believes that Alice saw X"; "Bob does not believe that Alice saw X"
  
• The experiment proved that the actual overhead of belief annotations can be significantly lower than their theoretic bound
  

Sigmod09-GrammarBasedDataCleaning-Arasu_kaushik

"A Grammar-based Entity Representation Framework for Data Cleaning", Arvind Arasu, Raghav Kaushik

• the framework presented is a programmable module that can be used to transform "dirty" input records to one or more "clean" output records with consistent representation of entities and sub-entities
• uses grammar based rules to decide which records are the same given the first name, last name and affiliation, knowing that all these information can be written in different ways although represent the same record.
  

ACM02DataQAssesment -Pipino

"Data Quality Assessment", L.Pipino, Y.Lee, R.Wang

Completeness can be defined as:

• schema completeness : entities and attributes are not missing from the schema
• column completeness : a function of missing values in a column of a table
• population completeness : given the set of values a column should have, count how many are missing
  

All can be measured as a ratio =1- #incomplet items/total #of items