Sabina Surdu

UbiWare: web-based dynamic data & service management platform for AmI

The surrounding space is constantly augmented by a myriad of devices that expose heterogeneous data, like slower-changing data, dynamic data streams and functionalities. Developing applications that cope with heterogeneous data and diverse communication protocols is a tedious task. The success of such applications depends on the performance of data access and on the easy management of available data. To address these challenges, we propose UbiWare, a middleware that facilitates application development for ambient intelligence. We abstract the surrounding space as a database-like environment and the heterogeneous entities and devices as data services that produce data. To query the distributed data services and access their data, we introduce an API that greatly simplifies application development and is compatible with the different operators used by query engines in Data Stream Management Systems or Pervasive Environment Management Systems.

ColisTrack: testbed for a pervasive environment management system

One of the leading challenges for pervasive computing is to ease the application development to smoothly handle the surrounding environment. We consider the case where the environment produces heterogeneous and continuous data, e. g. temperature readings, car positions... We have defined a scenario for containers transportation tracking in a medical context involving the transportation of fragile biological matter in sensor-enhanced containers. This scenario has been simulated as a testbed and offers a very nice setting to measure the agility of data-centric application development. On top of this scenario, we have built a pervasive application using a Pervasive Environment Management System called SoCQ (Service oriented Continuous Queries). SoCQ provides a data-oriented perspective of the pervasive environment, mixing classical data, streams and functionalities. For the demo, our objective is twofold: first, from the application developer point of view, she has access to the underlying SoCQ-schema and she may pose her own SQL-like queries to the simulated environment. Second, from the end-user point of view, she may quite easily interact with the environment either through a general dynamic visualization with Google Maps of hospitals, cars moving along roads and medical containers waiting or being transported, or by getting SMS notifications on her own phone of results of predefined queries.

UPnQ: An Architecture for Personal Information Exploration

Today our lives are being mapped to the binary realm provided by computing devices and their interconnections. The constant increase in both amount and diversity of personal information organized in digital files already turned into an information overload. User files contain an ever augmenting quantity of potential information that can be extracted at a non-negligible processing cost. In this paper we pursue the difficult objective of providing easy and efficient personal information management, in a file-oriented context. To this end, we propose the Universal Plug’n’Query (UPnQ) principled approach for Personal Information Management. UPnQ is based on a virtual database that offers query facilities over potential information from files while tuning resource usage. Our goal is to declaratively query the contents of dynamically discovered files at a fine-grained level. We present an architecture that supports our approach and we conduct a simulation study that explores different caching strategies.

Addressing resource usage in stream processing systems: sizing window effect

Stream processing systems compute continuous queries over increasingly large volumes of data, as monitoring applications emerge in a broad array of fields. These systems need to satisfy application-dependent constraints, one of the most important ones being accuracy demands and query response times. As system resources are limited, various query optimization techniques are proposed. To the best of our knowledge, none of the existing methods takes into account the size of the window, which is input to a query. We believe resource usage can be tackled with a novel approach, that attempts to compute an optimal window size for a given continuous query, thereby placing a minimal upper bound on the resource consumption for that query.