Key concepts of group pattern discovery algorithms from spatio-temporal trajectories

Over the years, the increasing development of location acquisition devices have generated a significant amount of spatio-temporal data. This data can be further analysed in search for some interesting patterns, new information, or to construct predictive models such as next location prediction. The goal of this paper is to contribute to the future research and development of group pattern discovery algorithms from spatio-temporal data by providing an insight into algorithms design in this research area which is based on a comprehensive classification of state-of-the-art models. This work includes static, big data as well as data stream processing models which to the best of authors’knowledge is the first attempt of presenting them in this context.Furthermore, the currently available surveys and taxonomies in this research area do not focus on group pattern mining algorithms nor include the state-of-the-art models. The authors conclude with the proposal of a conceptual model of Universal,Streaming, Distributed and Parameter-light (UDSP) algorithm that addresses current challenges in this research area

Sencar Based Load Balanced Clustering With Mobile Data Gathering In Wireless Sensor Networks

The wireless sensor networks consist of static sensors, which can be deployed in a wide environment for monitoring applications. While transmitting the data from source to static sink, the amount of energy consumption of the sensor node is high. This results in reduced lifetime of the network. Some of the WSN architectures have been proposed based on Mobile Elements such as three-layer framework is for mobile data collection, which includes the sensor layer, cluster head layer, and mobile collector layer (called SenCar layer). This framework employs distributed load balanced clustering and dual data uploading, it is referred to as LBC-DDU.In the sensor layer a distributed load balanced clustering algorithm is used for sensors to self-organize themselves into clusters. The cluster head layer use inter-cluster transmission range it is carefully chosen to guarantee the connectivity among the clusters. Multiple cluster heads within a cluster cooperate with each other to perform energy-saving in the inter-cluster communications. Through this transmissions cluster head information is send to the SenCar for its moving trajectory planning.This is done by utilizing multi-user multiple-input and multiple-output (MU-MIMO) technique. Then the results show each cluster has at most two cluster heads. LBC-DDU achieves higher energy saving per node and energy saving on cluster heads comparing with data collection through multi-hop relay to the static data sinks

Parallel Algorithms for Constrained Tensor Factorization via the Alternating Direction Method of Multipliers

Tensor factorization has proven useful in a wide range of applications, from sensor array processing to communications, speech and audio signal processing, and machine learning. With few recent exceptions, all tensor factorization algorithms were originally developed for centralized, in-memory computation on a single machine; and the few that break away from this mold do not easily incorporate practically important constraints, such as nonnegativity. A new constrained tensor factorization framework is proposed in this paper, building upon the Alternating Direction method of Multipliers (ADMoM). It is shown that this simplifies computations, bypassing the need to solve constrained optimization problems in each iteration; and it naturally leads to distributed algorithms suitable for parallel implementation on regular high-performance computing (e.g., mesh) architectures. This opens the door for many emerging big data-enabled applications. The methodology is exemplified using nonnegativity as a baseline constraint, but the proposed framework can more-or-less readily incorporate many other types of constraints. Numerical experiments are very encouraging, indicating that the ADMoM-based nonnegative tensor factorization (NTF) has high potential as an alternative to state-of-the-art approaches.Comment: Submitted to the IEEE Transactions on Signal Processin

Adapted K-Nearest Neighbors for Detecting Anomalies on Spatio–Temporal Traffic Flow

Outlier detection is an extensive research area, which has been intensively studied in several domains such as biological sciences, medical diagnosis, surveillance, and traffic anomaly detection. This paper explores advances in the outlier detection area by finding anomalies in spatio-temporal urban traffic flow. It proposes a new approach by considering the distribution of the flows in a given time interval. The flow distribution probability (FDP) databases are first constructed from the traffic flows by considering both spatial and temporal information. The outlier detection mechanism is then applied to the coming flow distribution probabilities, the inliers are stored to enrich the FDP databases, while the outliers are excluded from the FDP databases. Moreover, a k-nearest neighbor for distance-based outlier detection is investigated and adopted for FDP outlier detection. To validate the proposed framework, real data from Odense traffic flow case are evaluated at ten locations. The results reveal that the proposed framework is able to detect the real distribution of flow outliers. Another experiment has been carried out on Beijing data, the results show that our approach outperforms the baseline algorithms for high-urban traffic flow

An enhanced classifier system for autonomous robot navigation in dynamic environments

In many cases, a real robot application requires the navigation in dynamic environments. The navigation problem involves two main tasks: to avoid obstacles and to reach a goal. Generally, this problem could be faced considering reactions and sequences of actions. For solving the navigation problem a complete controller, including actions and reactions, is needed. Machine learning techniques has been applied to learn these controllers. Classifier Systems (CS) have proven their ability of continuos learning in these domains. However, CS have some problems in reactive systems. In this paper, a modified CS is proposed to overcome these problems. Two special mechanisms are included in the developed CS to allow the learning of both reactions and sequences of actions. The learning process has been divided in two main tasks: first, the discrimination between a predefined set of rules and second, the discovery of new rules to obtain a successful operation in dynamic environments. Different experiments have been carried out using a mini-robot Khepera to find a generalised solution. The results show the ability of the system to continuous learning and adaptation to new situations.Publicad

The Roads To and From Serfdom

The institution of slavery displays a puzzling historical pattern: it is found mostly at intermediate stages of agricultural development, in horticultural societies, and less frequently among hunter-gatherers and societies at more advanced agrarian stages. We explain this rise-and- fall pattern of slavery in a growth model with land and labor as inputs in production. The "organization" of society is determined endogenously, and depends on agricultural technology and population density, both of which also evolve endogenously over time, and depend on how society is organized. The model replicates the full transition of the economy from an egalitarian society with no property rights; to a slave society where a despotic ruler owns both land and people; and finally into a society with a free labor market, where the ruler owns all land but all agents own their labor. In this process, the role of population growth switches from being a force driving the transition into slavery, to a force behind the transition from slavery to free labor. Our model also explains several other historical facts, e.g. why Europeans replaced free labor with slavery following the discovery of the Americas, and why those states in the 19th century US which had sparser population had a larger percentage slaves in the population.Slavery, growth, intsitutions, population

The Roads To and From Serfdom

The institution of slavery displays a puzzling historical pattern: it is found mostly at intermediate stages of agricultural development, in horticultural societies, and less frequently among hunter-gatherers and societies at more advanced agrarian stages. We explain this rise-and- fall pattern of slavery in a growth model with land and labor as inputs in production. The ``organization'' of society is determined endogenously, and depends on agricultural technology and population density, both of which also evolve endogenously over time, and depend on how society is organized. The model replicates the full transition of the economy from an egalitarian society with no property rights; to a slave society where a despotic ruler owns both land and people; and finally into a society with a free labor market, where the ruler owns all land but all agents own their labor. In this process, the role of population growth switches from being a force driving the transition into slavery, to a force behind the transition from slavery to free labor. Our model also explains several other historical facts, e.g. why Europeans replaced free labor with slavery following the discovery of the Americas, and why those states in the 19th century US which had sparser population had a larger percentage slaves in the population.Growth, slavery, intitutions, population