Anna Ciampi

Università degli Studi di Bari Aldo Moro | Università di Bari · Department of Computer Science

Anomaly detection and change analysis are challenging tasks in stream data mining. We illustrate a novel method that addresses both these tasks in geophysical applications. The method is designed for numeric data routinely sampled through a sensor network. It extends the traditional time series forecasting theory by accounting for the spatial infor...

The management of massive amounts of geodata collected by sensor networks creates several challenges, including the real-time application of summarization techniques, which should allow the storage of this unbounded volume of georeferenced and timestamped data in a server with a limited memory for any future query. SUMATRA is a summarization techni...

A growing volume of geodata requires for appropriate data management systems, which ensure data acquisition and memory-preserving storage as well as continuous surveillance of this unbounded amount of georeferenced data. Trend cluster discovery, as a spatiotemporal aggregate operator, may play a crucial role in the surveillance process of the senso...

A PhotoVoltaic (PV) plant is a power station which converts sunlight energy into electric energy. In the last decade, PV plants have become ubiquitous in several countries of the European Union, due to a valuable policy of economic incentives (e.g., feed-in tariffs). Today, this ubiquity of PV plants has paved the way to the marketing of new smart...

Recent advances in pervasive computing and sensor technologies have significantly influenced the field of geosciences, by changing the type of dynamic environmental phenomena that can be detected, monitored, and reacted to. Another important aspect is the real-time data delivery of novel platforms. In this chapter, we describe the specific characte...

Ubiquitous sensor stations continuously measure several geophysical variables over large zones and long (potentially unbounded) periods of time. However, observations can cover neither every space location nor every time. Interpolation, i.e., the estimation of unknown data in each location or time of interest, can be used to supplement station reco...

Ubiquitous sensor stations continuously measure several geophysical fields over large zones and long (potentially unbounded) periods of time. However, observations can never cover every location nor every time. In addition, due to its huge volume, the data produced cannot be entirely recorded for future analysis. In this scenario, interpolation, i....

Advances in pervasive computing and sensor technologies have paved the way for the explosive living ubiquity of geo-physical data streams. The management of the massive and unbounded streams of sensor data produced poses several challenges, including the real-time application of summarization techniques, which should allow the storage and query of...

The trend cluster discovery retrieves areas of spatially close sensors which measure a numeric random field having a prominent data trend along a time horizon. We propose a computation preserving algorithm which employees an incremental learning strategy to continuously maintain sliding window trend clusters across a sensor network. Our proposal re...

Information acquisition in a pervasive sensor network is often affected by faults due to power outage at nodes, wrong time synchronizations, interference, network transmission failures, sensor hardware issues or excessive energy consumption for communications. These issues impose a trade-off between the precision of the measurements and the costs o...

The information acquisition in a pervasive sensor network is often affected by faults due to power outage at nodes, wrong time synchronizations, interference, network transmission failures, sensor hardware issues or excessive energy consumption for communications. These issues impose a trade-off between the precision of the measurements and the cos...

The rising need of energy to improve the quality of life has paved the way for the development and the incentive of different kinds of renewable energy technologies. In particular, the recent increase in the number of installed PhotoVoltaic (PV) plants has boosted the marketing of new monitoring systems designed to take under control the energy pro...

Spatio-temporal data collected in sensor networks are often affected by faults due to power outage at nodes, wrong time synchronizations, interference, network transmission failures, sensor hardware issues or high energy consumption during communications. Therefore, acquisition of information by wireless sensor networks is a challenging step in mon...

We define a new kind of stream cube, called geo-trend stream cube, which uses trends to aggregate a numeric measure which is streamed by a sensor network and is organized around space and time dimensions. We specify space-time roll-up and drill-down to explore trends at coarse grained and inner grained hierarchical view.

In many real-time applications, such as wireless sensor network monitoring, traffic control or health monitoring systems, it is required to analyze continuous and unbounded geographically distributed streams of data (e.g. temperature or humidity measurements transmitted by sensors of weather stations). Storing and querying geo-referenced stream dat...

We consider distributed computing environments where geo-referenced sensors feed a unique central server with numeric and uni-dimensional data streams. Knowledge discovery from these geographically distributed data streams poses several challenges including the requirement of data summarization in order to store the streamed data in a central serve...

A spatio-temporal data stream is a sequence of time-stamped geo-referenced data elements which arrive at consecutive time points. In addition to the spatial and temporal dimensions which are information bearing, stream poses further challenges to data mining, which are avoiding multiple scans of the entire data sets, optimizing memory usage, and mi...

Emerging real life applications, such as environmental compliance, ecological studies and meteorology, are characterized by real-time data acquisition through remote sensor networks. The most important aspect of the sensor readings is that they comprise a space dimension and a time dimension which are both information bearing. Additionally, they us...

Traditional algorithms for frequent itemset discovery are designed for static data. They cannot be straightforwardly applied to data streams which are continuous, unbounded, usually coming at high speed and often with a data distribution which changes with time. The main challenges of frequent pattern mining in data streams are: avoiding multiple s...

Some challenges in frequent pattern mining from data streams are the drift of data distribution and the computational efficiency. In this work an additional challenge is considered: data streams describe complex objects modeled by multiple database relations. A multi-relational data mining algorithm is proposed to efficiently discover approximate r...

INGENS is a prototype of GIS which integrates a geographic knowledge discovery engine to mine several kinds of spatial KDD objects from the topographic maps stored in a spatial database. In this paper we describe the main principles of an inductive spatial database in INGENS. Inductive database allows to keep permanent KDD objects and integrate dat...

The Web services stack of standards is designed to support the reuse and the interoperation of software components on the Web. A critical step in the process of developing applications based on the service oriented architecture is the service discovery. This paper shows how service composition can be used as a technique to support service discovery...

Many emerging applications are characterized by real-time stream data acquisition through sensors which have geographical loca-tions and/or spatial extents. Streaming prevents from storing all data from the stream and performing multiple scans of the entire data sets as normally done in traditional applications. The drift of data distribu-tion pose...