Anna Ciampi

An algorithm for Web service discovery through their composition

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. The paper discusses the current state of research in this area and introduces a semantic matching algorithm that exploits the possibility to compose multiple services in order to satisfy a service request.

Summarization for geographically distributed data streams

We consider distributed computing environments where georeferenced sensors feed a unique central server with numeric and unidimensional data streams. Knowledge discovery fromthese geographically distributed data streams poses several challenges including the requirement of data summarization in order to store the streamed data in a central server with a limited memory. We propose an enhanced segmentation algorithm in order to group data sources in the same spatial cluster if they stream data which evolve according to a close trajectory over the time. A trajectory is constructed by tracking only data points which represent a change of trend in the associated spatial cluster. Clusters of trajectories are discovered on-the-fly and stored in the database. Experiments prove effectiveness and accuracy of our approach.

Summarizing numeric spatial data streams by trend cluster discovery

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 this amount of georeferenced and timestamped data in a server with limited memory. In order to face this issue, we have designed a summarization technique, called SUMATRA, which segments the stream into windows, computes summaries window-by-window and stores these summaries in a database. Trend clusters are discovered as summaries of each window. They are clusters of georeferenced data which vary according to a similar trend along the window time horizon. Several compression techniques are also investigated to derive a compact, but accurate representation of these trends for storage in the database. A learning strategy to automatically choose the best trend compression technique is designed. Finally, an in-network modality for tree-based trend cluster discovery is investigated in order to achieve an efficacious aggregation schema which drastically reduces the number of bytes transmitted across the network and maintains a longer network lifespan. This schema is mapped onto the routing structure of a tree-based WSN topology. Experiments performed with several data streams of real sensor networks assess the summarization capability, the accuracy and the efficiency of the proposed summarization schema.

Dealing with temporal and spatial correlations to classify outliers in geophysical data streams

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 information of geophysical data. In particular, a forecasting model is computed incrementally by accounting for the temporal correlation of data which exhibit a spatial correlation in the recent past. For each sensor the observed value is compared to its spatial-aware forecast, in order to identify the outliers. Finally, the spatial correlation of outliers is analyzed, in order to classify changes and reduce the number of false anomalies. The performance of the presented method is evaluated in both artificial and real data streams.

Using trend clusters for spatiotemporal interpolation of missing data in a sensor network

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 producedcannot be entirelyrecordedfor futureanalysis. In this scenario, interpolation, i.e., the estimation of unknown data in each location or time of interest, can be used to supple- ment station records. Although in GIScience there has been a tendency to treat space and time separately, integrating space and time could yield better results than treating them separately when interpolating geophysical fields. According to this idea, a spatiotemporal interpolation process, which accounts for both space and time, is described here. It oper- ates in two phases. First, the exploration phase addresses the problem of interaction. This phase is performed on-line using data recordedfrom a network throughout a time window. The trend cluster discovery process determines prominent data trends and geographically- aware station interactions in the window. The result of this process is given before a new data window is recorded. Second, the estimation phase uses the inverse distance weighting approach both to approximate observed data and to estimate missing data. The proposed technique has been evaluated using two large real climate sensor networks. The experi- ments empirically demonstrate that, in spite of a notable reduction in the volume of data, the technique guarantees accurate estimation of missing data.

Trend cluster based compression of geographically distributed data streams

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 data poses specific challenges both in time (real-time processing) and in space (limited storage capacity). Summarization algorithms can be used to reduce the amount of data to be permanently stored into a data warehouse without losing information for further subsequent analysis. In this paper we present a framework in which data streams are seen as time-varying realizations of stochastic processes. Signal compression techniques, based on transformed domains, are applied and compared with a geometrical segmentation in terms of compression efficiency and accuracy in the subsequent reconstruction.

A Sliding Window Algorithm for Relational Frequent Patterns Mining from Data Streams

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 relational frequent patterns over a sliding time window of a complex data stream. The effectiveness of the method is proved on application to the Internet packet stream.

Integrating trend clusters for spatio-temporal interpolation of missing sensor data

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 of communication and processing which are directly proportional to the number of sensors and/or transmissions. We present a spatio-temporal interpolation technique which allows an accurate estimation of sensor network missing data by computing the inverse distance weighting of the trend cluster representation of the transmitted data. The trend-cluster interpolation has been evaluated in a real climate sensor network in order to prove the efficacy of our solution in reducing the amount of transmissions by guaranteeing accurate estimation of missing data.

An Intelligent System for Real Time Fault Detection in PV Plants

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 production of PV plants. In this paper, we present an intelligent monitoring system, called SUNInspector, which resorts to spatio-temporal data mining techniques, in order to monitor energy productions of PV plants and detect real-time possible plant faults. SUNInspector uses spatio-temporal patterns, called trend clusters, to model the trends according to the energy production of the PV plants varies depending on the region where it is installed (spatial dependence) and the period of the year of the measurements (temporal dipendence). Each time a PV plant transmits its energy production measurement, the risk of a plant fault is measured by evaluating the persistence of an high difference between the real production and the expected production. A case study with PV plants distributed over the South of Italy is illustrated.

Trend cluster based kriging interpolation in sensor data networks

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 monitoring physical ubiquitous phenomena (e.g. weather, pollution, traffic). This issue gives raise to a fundamental trade-off: higher density of sensors provides more data, higher resolution and better accuracy, but requires more communications and processing. A data mining approach to reduce communication and energy requirements is investigated: the number of transmitting sensors is decreased as much as possible, even keeping a reasonable degree of data accuracy. Kriging techniques and trend cluster discovery are employed to estimate unknown data in any un-sampled location of the space and at any time point of the past. Kriging is a statistical interpolation group of techniques, suited for spatial data, which estimates the unknown data in any space location by a proper weighted mean of nearby observed data. The trend clusters are stream patterns which compactly represent sensor data by means of spatial clusters having prominent data trends in time. Kriging is here applied to estimate unknown data taking into account a spatial correlation model of the sensor network. Trends are used as a guideline to transfer this model across the time horizon of the trend itself. Experiments are performed with a real sensor data network, in order to evaluate this interpolation technique and demonstrate that Kriging and trend clusters outperform, in terms of accuracy, interpolation competitors like Nearest Neighbor or Inverse Distance Weighting.