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From Databases to Dataspaces*Wearing the Linked Data goggles DERI reading group presentation 23.02.2011    PhD J. Umbrich *  M. Franklin, A. Halevy, D. Maier    in ACM SIGMOD Record, Dez. 2005
Background of the paper Motivation of the paper in 2005 The authors ,[object Object]
Development of relational database management systems showed spectacular results
BUT: “data everywhere” and use cases relying on large amount of diverse, interrelated data sources poses new challenges for the data management
M. Franklin: UC Berkeley, large scale data management
A. Halevy: Google Inc.usage of structured data in web search
D. Maier: Portland State Universitycoined Datalog, data stream processing 1 / 24
 Topic of the paper Dataspacesand their support systems as anew agenda for data management 2 / 24
 The Problem: Data Management Loosely connected data sources Information are available in various formats Not always control over data Low-level data management challenges across heterogeneous collections Search & querying Tracking lineage Availability & recovery Enforcing rules Integrity constraints Access control Naming convention (meta) data evolution  3 / 24
 The Solution Define space of data Identifiable scope and control across the data and underlying systems DataSpace Support Platforms (DSSPs)Offers a suite of interrelated services and guarantees over self managed data sources (no complete data control) Pay-as-you-go Keyword search is bare minimum More function and increased consistency as you add work 4 / 24
DataSpaces: System
DataSpaces: Logical Components data co-existence approach (not data integration) contains all information relevantto a particular organisation regardless of the format and location model a rich collection of relationships between data repositories Participants Relations ,[object Object]
RDBs, XML, text, services
Stored or streamed
Different query support
Support updates, read only
Any kind of relationship
A replica of B
C mapping for A and BBroader set of relations ,[object Object],5 / 24
DataSpaces: Services Content heterogeneity requires multiple style of data access Cataloging data resources (source, name, size, creation data, location)  Search as a primary mechanism to deal with large collections and unfamiliar data (Similarity search, ranking) Search applicable to all content of the dataspace regardless of data format (includes also meta data)  Updates (major research) Monitoring, event detection, support for complex workflows 6 / 24
DataSpaces: System Source: Franklin et al: From Databases to Dataspaces, SIGMOD Rec. 2005 7 / 24
 DSSP: Catalog ,[object Object]
Like (Rate of change, query answering, statistics, ownership, access, privacy policies, relationships
Basic inventory
Identifier, type, creation date
Answering presence, absence of data element
Model Management environment on top of the catalog8 / 24

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Reading Group: From Database to Dataspaces