COIN 2016 - First International Workshop on Consistency and Inconsistency

Consistency is an ubiquitous concern in all of computing, and particularly in databases, knowledge-based expert systems and related applications, e.g., of linked and big data. In each of these fields, inconsistency is commonplace. With the unrelented growth of data volumes in computing, the occurence of various forms of inconsistency is dramatically increasing. Many different approaches to prevent, work around, explain, live with, tame, diminish, repair, resolve, or even take advantage of extant inconsistencies, have been proposed or are being used, both in theory and in practice. An obvious example is the ongoing debate around eventual consistency, as manifested in many studies and whitepapers, as well as industrial implementations of NewSQL, NoSQL and big data store systems.
For satisfying the demands of applications and customers in terms of consistency maintenance and handling inconsistency, commercially motivated investigations and developments are leading the way, while theory is struggling to keep up with what is going on in practice. Hence, the COIN workshop intends to provide a forum for encounters of researchers and developers concerned about consistency and inconsistency in contemporary database systems and data-centric applications, for discussing and mutually inspiring each other, and thus for advancing the state of the art in the theory and practice of guaranteeing various degrees of consistency and trading off inconsistency.
Topics
We are calling for papers presenting original research, innovative ideas, work in progress, industrial experience, case studies or position statements. Also demonstrations and descriptions of prototype systems and implementations are welcome. Among many related issues, the topics of interst to COIN include the the following subjects and areas where inconsistency may occur.
Abductive, Deductive, Inductive Reasoning
ACID Transactions
Adaptive Logics
Ambiguous, Imprecise, Incomplete, Vague Data
Argumentation
Availabilty and Dependability
Belief Revision
Big Data
Circumscription
Cloud Computing
Communication, Collaboration, Interaction
Counterfactual Reasoning
Data Analysis
Data Cleansing
Data Mining
Data Modeling and Knowledge Representation
Data Quality
Data Science
Datalog
Default Reasoning
Degrees of Consistency and Inconsistency
Description Logic
Diagnosis
Direct Logic
Eventual Consistency
Distribution and Replication
Fault Tolerance and Recovery
Forensics
Hypotetical Reasoning
Inconsistency Metrics
Inconsistency Tolerance
Information Retrieval
Integrity Maintenance
Legacy Data
Linked Data
Modalities of Consistency and Inconsistency
Natural Language Understanding
Nonmonotonic Reasoning
NoSQL
Null Values
Ontology Alignment and Data Integration
Paraconsistent Logics
Planning
Provenance of Data
Quantum Information Theory and Processing
Quasi-classical logic
Remedying and Repairing
Requirement Engineering
Risk Management
Rough, Noisy, Messy, Fuzzy Data
Rules and Exceptions
Security
Semantic Web
Semi- and Unstructured Data
Streaming Data
Truth Maintenance