Exploring sensor data management

The increasing availability of cheap, small, low-power sensor hardware and the ubiquity of wired and wireless networks has led to the prediction that `smart evironments' will emerge in the near future. The sensors in these environments collect detailed information about the situation people are in, which is used to enhance information-processing applications that are present on their mobile and `ambient' devices.\ud \ud Bridging the gap between sensor data and application information poses new requirements to data management. This report discusses what these requirements are and documents ongoing research that explores ways of thinking about data management suited to these new requirements: a more sophisticated control flow model, data models that incorporate time, and ways to deal with the uncertainty in sensor data

Multi-scale window specification over streaming trajectories

Enormous amounts of positional information are collected by monitoring applications in domains such as fleet management cargo transport wildlife protection etc. With the advent of modern location-based services processing such data mostly focuses on providing real-time response to a variety of user requests in continuous and scalable fashion. An important class of such queries concerns evolving trajectories that continuously trace the streaming locations of moving objects like GPS-equipped vehicles commodities with RFID\u27s people with smartphones etc. In this work we propose an advanced windowing operator that enables online incremental examination of recent motion paths at multiple resolutions for numerous point entities. When applied against incoming positions this window can abstract trajectories at coarser representations towards the past while retaining progressively finer features closer to the present. We explain the semantics of such multi-scale sliding windows through parameterized functions that reflect the sequential nature of trajectories and can effectively capture their spatiotemporal properties. Such window specification goes beyond its usual role for non-blocking processing of multiple concurrent queries. Actually it can offer concrete subsequences from each trajectory thus preserving continuity in time and contiguity in space along the respective segments. Further we suggest language extensions in order to express characteristic spatiotemporal queries using windows. Finally we discuss algorithms for nested maintenance of multi-scale windows and evaluate their efficiency against streaming positional data offering empirical evidence of their benefits to online trajectory processing

Data science applications to connected vehicles: Key barriers to overcome

The connected vehicles will generate huge amount of pervasive and real time data, at very high frequencies. This poses new challenges for Data science. How to analyse these data and how to address short-term and long-term storage are some of the key barriers to overcome.JRC.C.6-Economics of Climate Change, Energy and Transpor

Constructing fading histograms from data streams

The ability to collect data is changing drastically. Nowadays, data are gathered in the form of transient and finite data streams. Memory restrictions preclude keeping all received data in memory. When dealing with massive data streams, it is mandatory to create compact representations of data, also known as synopses structures or summaries. Reducing memory occupancy is of utmost importance when handling a huge amount of data. This paper addresses the problem of constructing histograms from data streams under error constraints. When constructing online histograms from data streams there are two main characteristics to embrace: the updating facility and the error of the histogram. Moreover, in dynamic environments, besides the need of compact summaries to capture the most important properties of data, it is also essential to forget old data. Therefore, this paper presents sliding histograms and fading histograms, an abrupt and a smooth strategies to forget outdated data