A Sparsity-Aware Adaptive Algorithm for Distributed Learning

In this paper, a sparsity-aware adaptive algorithm for distributed learning in diffusion networks is developed. The algorithm follows the set-theoretic estimation rationale. At each time instance and at each node of the network, a closed convex set, known as property set, is constructed based on the received measurements; this defines the region in which the solution is searched for. In this paper, the property sets take the form of hyperslabs. The goal is to find a point that belongs to the intersection of these hyperslabs. To this end, sparsity encouraging variable metric projections onto the hyperslabs have been adopted. Moreover, sparsity is also imposed by employing variable metric projections onto weighted $\ell_1$ balls. A combine adapt cooperation strategy is adopted. Under some mild assumptions, the scheme enjoys monotonicity, asymptotic optimality and strong convergence to a point that lies in the consensus subspace. Finally, numerical examples verify the validity of the proposed scheme, compared to other algorithms, which have been developed in the context of sparse adaptive learning

Big Data Management and Analytics for Mobility Forecasting in datAcron

The exploitation of heterogeneous data sources offering very large historical and streaming data is important to increasing the accuracy of operations when analysing and predicting future states of moving entities (planes, vessels, etc.). This article presents the overall goals and big data challenges addressed by datAcron on big data analytics for time-critical mobility forecasting

Optimal Time-dependent Sequenced Route Queries in Road Networks

In this paper we present an algorithm for optimal processing of time-dependent sequenced route queries in road networks, i.e., given a road network where the travel time over an edge is time-dependent and a given ordered list of categories of interest, we find the fastest route between an origin and destination that passes through a sequence of points of interest belonging to each of the specified categories of interest. For instance, considering a city road network at a given departure time, one can find the fastest route between one's work and his/her home, passing through a bank, a supermarket and a restaurant, in this order. The main contribution of our work is the consideration of the time dependency of the network, a realistic characteristic of urban road networks, which has not been considered previously when addressing the optimal sequenced route query. Our approach uses the A* search paradigm that is equipped with an admissible heuristic function, thus guaranteed to yield the optimal solution, along with a pruning scheme for further reducing the search space. In order to compare our proposal we extended a previously proposed solution aimed at non-time dependent sequenced route queries, enabling it to deal with the time-dependency. Our experiments using real and synthetic data sets have shown our proposed solution to be up to two orders of magnitude faster than the temporally extended previous solution.Comment: 10 pages, 12 figures To be published as a short paper in the 23rd ACM SIGSPATIA

Segmentation and sampling of moving object trajectories based on representativeness.

International audienceMoving Object Databases (MOD), although ubiquitous, still call for methods that will be able to understand, search, analyze, and browse their spatiotemporal content. In this paper, we propose a method for trajectory segmentation and sampling based on the representativeness of the (sub-)trajectories in the MOD. In order to find the most representative sub-trajectories, the following methodology is proposed. First, a novel global voting algorithm is performed, based on local density and trajectory similarity information. This method is applied for each segment of the trajectory, forming a local trajectory descriptor that represents line segment representativeness. The sequence of this descriptor over a trajectory gives the voting signal of the trajectory, where high values correspond to the most representative parts. Then, a novel segmentation algorithm is applied on this signal that automatically estimates the number of partitions and the partition borders, identifying homogenous partitions concerning their representativeness. Finally, a sampling method over the resulting segments yields the most representative sub-trajectories in the MOD. Our experimental results in synthetic and real MOD verify the effectiveness of the proposed scheme, also in comparison with other sampling techniques

On-the-fly mobility event detection over aircraft trajectories

We present an application framework that consumes streaming positions from a large fleet of flying aircrafts monitored in real time over a wide geographical area. Tailored for aviation surveillance, this online processing scheme only retains locations conveying salient mobility events along each flight, and annotates them as stop, change of speed, heading or altitude, etc. Such evolving trajectory synopses must keep in pace with the incoming raw streams so as to get incrementally annotated with minimal loss in accuracy. We also develop one-pass heuristics to eliminate inherent noise and provide reliable trajectory representations. Our prototype implementation on top of Apache Flink and Kafka has been tested against various real and synthetic datasets offering concrete evidence of its timeliness, scalability, and compression efficiency, with tolerable concessions to the quality of resulting trajectory approximations. K. Patroumpas, N. Pelekis, and Y. Theodoridis: "On-the-fly Mobility Event Detection over Aircraft Trajectories". In proceeding of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2018), November 6 - 9, 2018 Seattle, Washington, USA Document type: Conference objec

GF-Miner: a Genetic Fuzzy Classifier for Numerical Data

Abstract Fuzzy logic and genetic algorithms are well-established computational techniques that have been employed to deal with the problem of classification as this is presented in the context of data mining. Based on Fuzzy Miner which is a recently proposed state-of-the-art fuzzy rule based system for numerical data, in this paper we propose GF-Miner which is a genetic fuzzy classifier that improves Fuzzy Miner firstly by adopting a clustering method for succeeding a more natural fuzzy partitioning of the input space, and secondly by optimizing the resulting fuzzy if-then rules with the use of genetic algorithms. More specifically, the membership functions of the fuzzy partitioning are extracted in an unsupervised way by using the fuzzy c-means clustering algorithm, while the extracted rules are optimized in terms of the volume of the rulebase and the size of each rule, using two appropriately designed genetic algorithms. The efficiency of our approach is demonstrated through an extensive experimental evaluation using the IRIS benchmark dataset