Aspect-oriented Data. Mika Mannermaa Ora Lassila. Nokia Services. Invited

Transcription

1 Aspect-oriented Data Mika Mannermaa Ora Lassila Nokia Services Invited

2 Nokia Services organization Services organization is responsible for various services under the Ovi brand Today s talk focus on provenance and aspect oriented data Nokia

3 Introduction: Why Provenance? What is provenance? When is it useful to track provenance? data comes from multiple sources data has multiple owners (in the legal sense, independent of sources) data is subject to one or more policies With Smart-M3 and NoTA moving forward we hypothesize that multi-source, multi-owner use cases will include more and more data from embedded systems, making provenance issues even more important. Because data is rich and coming from multiple sources, we need a flexible data infrastructure where domain schemata can evolve individually Semantic web technologies seem interesting choice for us Nokia

4 RDF and Provenance RDF is a representation framework where schemata (models) and the data (as a graph) are decoupled this is a feature of the RDF design Standard comes with a way to represent provenance (reification) Many other attempts to tackle provenance, all essentially same as reification (attach provenance information to a group of triples rather than a single triple) named graphs quoting molecules Formal semantics of RDF do not specify what happens when you mix domain-level data and provenance data Nokia

5 Aspect-Oriented Data Aspect-Oriented Programming [Kiczales 1996] cross-cutting aspects that do not lend themselves to be implemented within the selected decomposition Tyranny of Dominant Decomposition Some examples Security Transactions Thread-based logging Data model design exhibits similar issues solution: decouple domain models from the storage schema (i.e., meta model) provide a view of raw data Provenance is typically an aspect Nokia

6 RDF and Aspect- Oriented Data RDF storage often uses a triple-based approach (graph edges as 3-tuples) Aspects could be attached to these tuples Example: Object A with properties x and y : Typical Relational Schema RDF Triples record-level metadata finer granularity for metadata object x y meta A B C object attr value meta A x B A y C Nokia

7 Aspect-Oriented Data, Policies and Synchronization A security boundary can be defined as [ ] a set of systems that are under a single administrative control. These boundaries occur at various levels, and vulnerabilities can become apparent as data crosses each boundary. Each system of record or security boundary defines their own domain-schemata and various policies to govern the data and its usage independently of others E.g. access control, information sharing, temporal relevancy, consistency, etc. Typically each system requires contractual obligations before releasing data to another system, and in practice this requires enforcing certain policies on data based on a source aspect in addition to owner based policies. In distributed multi-source systems sharing just the data is not enough. Policies governing the data are required as well Nokia

8 Meta-schema and the Choice of Aspects RDF and other semi-structured formalisms enjoy great flexibility wrt. schema changes Should we have the same for the aspects (i.e., the meta-schema the tuple-based approach presented here seems to preclude this We hypothesize that the meta-schema will change less (and slower) than domain models Our current set of aspect candidates : source where did the data come from owner who owns the data in the legal sense time when was the data added (possibly also some quality metrics ) Nokia

9 Conclusions Cross-cutting aspects manifest in data model design just like in software decomposition Aspects can be used to implement provenance RDF and the tuple-based storage of graph data are well suited to implement aspects This is ongoing work, and many issues remain: should we consider time separately, are there special queries related to time? how to get triple-store vendors to support this (AOD would be easy to implement if you are building your own triple store ) scalability (wrt. storage, wrt. policy evaluation) Smart-M3 infrastructure provides a good framework for applying results of provenance research Smart-M3 can result in new business opportunities as embedded data becomes easily reachable by web tools Nokia

10 Questions? Nokia

11 Nokia V1-Filename.ppt / YYYY-MM-DD / Initials Thank you