Category: Publications

“Information Systems Integration and Evolution: Ontologies at Rescue”, Carlo A. Curino, Letizia Tanca, Carlo Zaniolo International Workshop on Semantic Technologies for System Maintenance (STSM) 2008

The life of a modern Information System is often characterized by integration with other systems, and continuous evolution of their data management core, to face unexpected changes in its requirements. Most of the solutions dealing with these issues from a database perspective rely on the notions of mapping and query rewriting. This paper presents the research agenda of ADAM (Advanced Data And Metadata Manager), a unified framework which, harvesting recent theoretical advances in this area, will deliver practical solutions for automatic schema mapping and graceful schema evolution. Information Systems evolution reflects changes in the model of their business reality; in the ADAM project we exploit ontologies to capture such changes in order to provide traceability and documentation of the evolution. Initial results and immediate benefits of this approach are presented.

Paper available at: curino-STSM08-CR.pdf

 Together with Marco Santambrogio and Donatella Sciuto, we describe the experience of the DRESD project, and the integration of JHDL in our platform and the results in term of education. The paper has been accepted for publication at the European Workshop on Microeletronics Education (EWME) 2008

“Context-aware views for mobile users” Cristiana Bolchini, Carlo A. Curino, Giorgio Orsi, Elisa Quintarelli, Rosalba Rossato, Fabio A. Schreiber and Letizia Tanca, (Extended Abstract) 10th DELOS Thematic Workshop on Personalized Access, Profile Management, and Context Awareness in Digital Libraries (PERSDL 2007)

ABSTRACT:

Independent, heterogeneous, distributed, sometimes tran-
sient and mobile data sources produce an enormous
amount of information that should be semantically inte-
grated and filtered, or, as we say, tailored, based on the
users’ interests and context. We propose to exploit knowl-
edge about the user, the adopted device, and the environ-
ment – altogether called context – to the end of informa-
tion tailoring. This paper presents the Context Dimension
Tree, a context model which is the basis for solving the
information tailoring problem, along with its role in the
framework of the Context-ADDICT architecture.

“Emergent Semantics and Cooperation in MultiKnowledge Environments: the ESTEEM Architecture.” Esteem Team. In Proc. of the VLDB Int. Workshop on Semantic Data and Service Integration (SDSI 2007), Vienna, Austria, 2007.

 ABSTRACT:

 In the present global society, information has to be exchange-
able in open and dynamic environments, where interacting
peers do not necessarily share a common understanding of
the world at hand, and do not have a complete picture of
the context where the interaction occurs. In this paper, we
present the Esteem approach and the related peer archi-
tecture for emergent semantics in dynamic and multi-know-
ledge environments. In Esteem, semantic communities are
built around declared interests in the form of manifesto on-
tologies, and their autonomous nature is preserved by al-
lowing a shared semantics to naturally emerge from peer
interactions.

2006 “Context Integration for Mobile Data Tailoring” (Extended Abstract) Cristiana Bolchini, Carlo A. Curino, Fabio A. Schreiber, Letizia Tanca, Proceedings of the Italian Symposium on Advanced Database Systems (SEBD) 2006

ABSTRACT:

Independent, heterogeneous, distributed, sometimes transient and mobile data sources produce an enormous amount of information that should be semantically integrated and filtered, or, as we say, tailored, based on the user’s interests and context. Since both the user and the data sources can be mobile, and the communication might be unreliable, caching the information on the user device may become really useful. Therefore new challenges have to be faced such as: data filtering in a context-aware fashion, integration of not-known-in-advance data sources, automatic extraction of the semantics. We propose a novel system named Context-ADDICT (Context-Aware Data Design, Integration, Customization and Tailoring) able to deal with the described scenario. The system we are designing aims at tailoring the available information to the needs of the current user in the current context, in order to offer a more manageable amount of information; such information is to be cached on the user’s device according to policies defined at design-time, to cope with data source transiency. This paper focuses on the information representation and tailoring problem and on the definition of the global architecture of the system.

BIBTEX:

@inproceedings{MDM2006,

author = {Cristiana Bolchini and Carlo Curino and Fabio A. Schreiber and Letizia Tanca}, 
Booktitle = {Proc. IEEE/ACM of Int. Conf. on Mobile Data Management}, 
Keywords = {Contex-ADDICT scenario and architecture}, 
Location = {Nara, Japan}, 
Month = {May}, 
Organization = {IEEE, ACM}, 
Title = {Context integration for mobile data tailoring}, 
doi = {http://dx.doi.org/10.1109/MDM.2006.52}, 
pdf = {mdm2006.pdf}, 
Year = {2006}

} 

“X-SOM: Mixing Ontology Mapping Strategies and Consistency Resolution ” Carlo A. Curino, Giorgio Orsi, Letizia Tanca, Poster at European Semantic Web Conferente (ESWC) 2007

“Mining Officially Unrecognized Side effects of Drugs by Combining Web Search and Machine Learning”, Carlo A. Curino, Clement Yu, Yuanyuan Liu, ACM “Conference on Information and Knowledge Management“ CIKM 2005

ABSTRACT:

We consider the problem of finding officially unrecognized side effects of drugs. By submitting queries to the Web involving a given drug name, it is possible to retrieve pages concerning the drug. However, many retrieved pages are irrelevant and some relevant pages are not retrieved. More relevant pages can be obtained by adding the active ingredient of the drug to the query. In order to eliminate irrelevant pages, we propose a machine learning process to filter out the undesirable pages. The process is shown experimentally to be very effective. Since obtaining training data for the machine learning process can be time consuming and expensive, we provide an automatic method to generate the training data. The method is also shown to be very accurate. The side effects of three drugs which are not recognized by FDA are validated by an expert. We believe that the same approach can be applied to many real life problems and will yield high precision. Thus, this could lead a new way to perform retrieval with high accuracy.

BIBTEX:

@inproceedings{1099670,
 author = {Carlo A. Curino and Yuanyuan Jia and Bruce Lambert and Patricia M. West and Clement Yu},
 title = {Mining officially unrecognized side effects of drugs by combining web search and machine learning},
 booktitle = {CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management},
 year = {2005},
 isbn = {1-59593-140-6},
 pages = {365--372},
 location = {Bremen, Germany},
 doi = {http://doi.acm.org/10.1145/1099554.1099670},
 publisher = {ACM},
 address = {New York, NY, USA},
 }

“TinyLIME: Bridging Mobile and Sensor Networks through Middleware”, Carlo A. Curino, Matteo Giani, Marco Giorgetta, Alessandro Giusti, Gian Pietro Picco, Amy L. Murphy. IEEE International Conference on Pervasive Computing and Communications. (PerCom) 2005

ABSTRACT:

In the rapidly developing field of sensor networks, bridging the gap between the applications and the hardware presents a major challenge. Although middleware is one solution, it must be specialized to the qualities of sensor networks, especially energy consumption. The work presented here provides two contributions: a new operational setting for sensor networks and a middleware for easing software development in this setting. The operational setting we target removes the usual assumption of a central collection point for sensor data. Instead the sensors are sparsely distributed in an environment, not necessarily able to communicate among themselves, and a set of clients move through space accessing the data of sensors nearby, yielding a system which naturally provides context relevant information to client applications. We further assume the clients are wirelessly networked and share locally accessed data. This scenario is relevant, for example, when relief workers access the information in their zone and share this information with other workers. Our second contribution, the middleware itself is an extension of LlME, our earlier work on middleware for mobile ad hoc networks. The model makes sensor data available through a tuple space interface, providing the illusion of shared memory between applications and sensors. This paper presents both the model and the implementation of our middleware incorporated with the Crossbow Mote sensor platform. 

 BIBTEX: 

@ARTICLE{1392743,
title={TinyLIME: bridging mobile and sensor networks through middleware},
author={Curino, C.; Giani, M.; Giorgetta, M.; Giusti, A.; Murphy, A.L.; Picco, G.P.},
journal={Pervasive Computing and Communications, 2005. PerCom 2005. Third IEEE International Conference on},
year={8-12 March 2005},
volume={},
number={},
pages={ 61-72},
keywords={ ad hoc networks, middleware, mobile computing, shared memory systems, software engineering, wireless sensor networks Crossbow Mote sensor platform, TinyLIME, middleware, mobile ad hoc networks, sensor networks, shared memory, software development, tuple space interface},
doi={10.1109/PERCOM.2005.48},
ISSN={}, }