Data stream mining in ubiquitous environments: state-of-the-art and current directions

In this article, we review the state-of-the-art techniques in mining data streams for mobile and ubiquitous environments. We start the review with a concise background of data stream processing, presenting the building blocks for mining data streams. In a wide range of applications, data streams are required to be processed on small ubiquitous devices like smartphones and sensor devices. Mobile and ubiquitous data mining target these applications with tailored techniques and approaches addressing scarcity of resources and mobility issues. Two categories can be identified for mobile and ubiquitous mining of streaming data: single-node and distributed. This survey will cover both categories. Mining mobile and ubiquitous data require algorithms with the ability to monitor and adapt the working conditions to the available computational resources. We identify the key characteristics of these algorithms and present illustrative applications. Distributed data stream mining in the mobile environment is then discussed, presenting the Pocket Data Mining framework. Mobility of users stimulates the adoption of context-awareness in this area of research. Context-awareness and collaboration are discussed in the Collaborative Data Stream Mining, where agents share knowledge to learn adaptive accurate models.

Towards a parallel computationally efficient approach to scaling up data stream classification

Advances in hardware technologies allow to capture and process data in real-time and the resulting high throughput data streams require novel data mining approaches. The research area of Data Stream Mining (DSM) is developing data mining algorithms that allow us to analyse these continuous streams of data in real-time. The creation and real-time adaption of classification models from data streams is one of the most challenging DSM tasks. Current classifiers for streaming data address this problem by using incremental learning algorithms. However, even so these algorithms are fast, they are challenged by high velocity data streams, where data instances are incoming at a fast rate. This is problematic if the applications desire that there is no or only a very little delay between changes in the patterns of the stream and absorption of these patterns by the classifier. Problems of scalability to Big Data of traditional data mining algorithms for static (non streaming) datasets have been addressed through the development of parallel classifiers. However, there is very little work on the parallelisation of data stream classification techniques. In this paper we investigate K-Nearest Neighbours (KNN) as the basis for a real-time adaptive and parallel methodology for scalable data stream classification tasks.

Pocket data mining: towards collaborative data mining in mobile computing environments

Pocket Data Mining (PDM) is our new term describing collaborative mining of streaming data in mobile and distributed computing environments. With sheer amounts of data streams are now available for subscription on our smart mobile phones, the potential of using this data for decision making using data stream mining techniques has now been achievable owing to the increasing power of these handheld devices. Wireless communication among these devices using Bluetooth and WiFi technologies has opened the door wide for collaborative mining among the mobile devices within the same range that are running data mining techniques targeting the same application. This paper proposes a new architecture that we have prototyped for realizing the significant applications in this area. We have proposed using mobile software agents in this application for several reasons. Most importantly the autonomic intelligent behaviour of the agent technology has been the driving force for using it in this application. Other efficiency reasons are discussed in details in this paper. Experimental results showing the feasibility of the proposed architecture are presented and discussed.

Distributed hoeffding trees for pocket data mining

Collaborative mining of distributed data streams in a mobile computing environment is referred to as Pocket Data Mining PDM. Hoeffding trees techniques have been experimentally and analytically validated for data stream classification. In this paper, we have proposed, developed and evaluated the adoption of distributed Hoeffding trees for classifying streaming data in PDM applications. We have identified a realistic scenario in which different users equipped with smart mobile devices run a local Hoeffding tree classifier on a subset of the attributes. Thus, we have investigated the mining of vertically partitioned datasets with possible overlap of attributes, which is the more likely case. Our experimental results have validated the efficiency of our proposed model achieving promising accuracy for real deployment.

Distributed classification for pocket data mining

Distributed and collaborative data stream mining in a mobile computing environment is referred to as Pocket Data Mining PDM. Large amounts of available data streams to which smart phones can subscribe to or sense, coupled with the increasing computational power of handheld devices motivates the development of PDM as a decision making system. This emerging area of study has shown to be feasible in an earlier study using technological enablers of mobile software agents and stream mining techniques [1]. A typical PDM process would start by having mobile agents roam the network to discover relevant data streams and resources. Then other (mobile) agents encapsulating stream mining techniques visit the relevant nodes in the network in order to build evolving data mining models. Finally, a third type of mobile agents roam the network consulting the mining agents for a final collaborative decision, when required by one or more users. In this paper, we propose the use of distributed Hoeffding trees and Naive Bayes classifers in the PDM framework over vertically partitioned data streams. Mobile policing, health monitoring and stock market analysis are among the possible applications of PDM. An extensive experimental study is reported showing the effectiveness of the collaborative data mining with the two classifers.

Homogeneous and heterogeneous distributed classification for pocket data mining

Pocket Data Mining (PDM) describes the full process of analysing data streams in mobile ad hoc distributed environments. Advances in mobile devices like smart phones and tablet computers have made it possible for a wide range of applications to run in such an environment. In this paper, we propose the adoption of data stream classification techniques for PDM. Evident by a thorough experimental study, it has been proved that running heterogeneous/different, or homogeneous/similar data stream classification techniques over vertically partitioned data (data partitioned according to the feature space) results in comparable performance to batch and centralised learning techniques.

eRules: a modular adaptive classification rule learning algorithm for data streams

Advances in hardware and software in the past decade allow to capture, record and process fast data streams at a large scale. The research area of data stream mining has emerged as a consequence from these advances in order to cope with the real time analysis of potentially large and changing data streams. Examples of data streams include Google searches, credit card transactions, telemetric data and data of continuous chemical production processes. In some cases the data can be processed in batches by traditional data mining approaches. However, in some applications it is required to analyse the data in real time as soon as it is being captured. Such cases are for example if the data stream is infinite, fast changing, or simply too large in size to be stored. One of the most important data mining techniques on data streams is classification. This involves training the classifier on the data stream in real time and adapting it to concept drifts. Most data stream classifiers are based on decision trees. However, it is well known in the data mining community that there is no single optimal algorithm. An algorithm may work well on one or several datasets but badly on others. This paper introduces eRules, a new rule based adaptive classifier for data streams, based on an evolving set of Rules. eRules induces a set of rules that is constantly evaluated and adapted to changes in the data stream by adding new and removing old rules. It is different from the more popular decision tree based classifiers as it tends to leave data instances rather unclassified than forcing a classification that could be wrong. The ongoing development of eRules aims to improve its accuracy further through dynamic parameter setting which will also address the problem of changing feature domain values.

Computationally efficient rule-based classification for continuous streaming data

Advances in hardware and software technologies allow to capture streaming data. The area of Data Stream Mining (DSM) is concerned with the analysis of these vast amounts of data as it is generated in real-time. Data stream classification is one of the most important DSM techniques allowing to classify previously unseen data instances. Different to traditional classifiers for static data, data stream classifiers need to adapt to concept changes (concept drift) in the stream in real-time in order to reflect the most recent concept in the data as accurately as possible. A recent addition to the data stream classifier toolbox is eRules which induces and updates a set of expressive rules that can easily be interpreted by humans. However, like most rule-based data stream classifiers, eRules exhibits a poor computational performance when confronted with continuous attributes. In this work, we propose an approach to deal with continuous data effectively and accurately in rule-based classifiers by using the Gaussian distribution as heuristic for building rule terms on continuous attributes. We show on the example of eRules that incorporating our method for continuous attributes indeed speeds up the real-time rule induction process while maintaining a similar level of accuracy compared with the original eRules classifier. We termed this new version of eRules with our approach G-eRules.

Fast adaptive real-time classification for data streams with concept drift

An important application of Big Data Analytics is the real-time analysis of streaming data. Streaming data imposes unique challenges to data mining algorithms, such as concept drifts, the need to analyse the data on the fly due to unbounded data streams and scalable algorithms due to potentially high throughput of data. Real-time classification algorithms that are adaptive to concept drifts and fast exist, however, most approaches are not naturally parallel and are thus limited in their scalability. This paper presents work on the Micro-Cluster Nearest Neighbour (MC-NN) classifier. MC-NN is based on an adaptive statistical data summary based on Micro-Clusters. MC-NN is very fast and adaptive to concept drift whilst maintaining the parallel properties of the base KNN classifier. Also MC-NN is competitive compared with existing data stream classifiers in terms of accuracy and speed.

DTM generation from LIDAR data using skewness balancing

LIght Detection And Ranging (LIDAR) data for terrain and land surveying has contributed to many environmental, engineering and civil applications. However, the analysis of Digital Surface Models (DSMs) from complex LIDAR data is still challenging. Commonly, the first task to investigate LIDAR data point clouds is to separate ground and object points as a preparatory step for further object classification. In this paper, the authors present a novel unsupervised segmentation algorithm-skewness balancing to separate object and ground points efficiently from high resolution LIDAR point clouds by exploiting statistical moments. The results presented in this paper have shown its robustness and its potential for commercial applications.

Big social data and political sentiment: the tweet stream during the UK General Election 2015 campaign

The General Election for the 56th United Kingdom Parliament was held on 7 May 2015. Tweets related to UK politics, not only those with the specific hashtag ”#GE2015”, have been collected in the period between March 1 and May 31, 2015. The resulting dataset contains over 28 million tweets for a total of 118 GB in uncompressed format or 15 GB in compressed format. This study describes the method that was used to collect the tweets and presents some analysis, including a political sentiment index, and outlines interesting research directions on Big Social Data based on Twitter microblogging.

Modeling the mechanisms that control in-stream dissolved organic carbon dynamics in upland and forested catchments

[1] We present a new, process-based model of soil and stream water dissolved organic carbon (DOC): the Integrated Catchments Model for Carbon (INCA-C). INCA-C is the first model of DOC cycling to explicitly include effects of different land cover types, hydrological flow paths, in-soil carbon biogeochemistry, and surface water processes on in-stream DOC concentrations. It can be calibrated using only routinely available monitoring data. INCA-C simulates daily DOC concentrations over a period of years to decades. Sources, sinks, and transformation of solid and dissolved organic carbon in peat and forest soils, wetlands, and streams as well as organic carbon mineralization in stream waters are modeled. INCA-C is designed to be applied to natural and seminatural forested and peat-dominated catchments in boreal and temperate regions. Simulations at two forested catchments showed that seasonal and interannual patterns of DOC concentration could be modeled using climate-related parameters alone. A sensitivity analysis showed that model predictions were dependent on the mass of organic carbon in the soil and that in-soil process rates were dependent on soil moisture status. Sensitive rate coefficients in the model included those for organic carbon sorption and desorption and DOC mineralization in the soil. The model was also sensitive to the amount of litter fall. Our results show the importance of climate variability in controlling surface water DOC concentrations and suggest the need for further research on the mechanisms controlling production and consumption of DOC in soils.

Water quality and ecology of the River Lee: mass balance and a review of temporal and spatial data

A regional overview of the water quality and ecology of the River Lee catchment is presented. Specifically, data describing the chemical, microbiological and macrobiological water quality and fisheries communities have been analysed, based on a division into river, sewage treatment works, fish-farm, lake and industrial samples. Nutrient enrichment and the highest concentrations of metals and micro-organics were found in the urbanised, lower reaches of the Lee and in the Lee Navigation. Average annual concentrations of metals were generally within environmental quality standards although, oil many occasions, concentrations of cadmium, copper, lead, mercury and zinc were in excess of the standards. Various organic substances (used as herbicides, fungicides, insecticides, chlorination by-products and industrial solvents) were widely detected in the Lee system. Concentrations of ten micro-organic substances were observed in excess of their environmental quality standards, though not in terms of annual averages. Sewage treatment works were the principal point source input of nutrients. metals and micro-organic determinands to the catchment. Diffuse nitrogen sources contributed approximately 60% and 27% of the in-stream load in the upper and lower Lee respectively, whereas approximately 60% and 20% of the in-stream phosphorus load was derived from diffuse sources in the upper and lower Lee. For metals, the most significant source was the urban runoff from North London. In reaches less affected by effluent discharges, diffuse runoff from urban and agricultural areas dominated trends. Flig-h microbiological content, observed in the River Lee particularly in urbanised reaches, was far in excess of the EC Bathing Water Directive standards. Water quality issues and degraded habitat in the lower reaches of the Lee have led to impoverished aquatic fauna but, within the mid-catchment reaches and upper agricultural tributaries, less nutrient enrichment and channel alteration has permitted more diverse aquatic fauna.

Geomorphological Map of Ribbed Moraines on the Dubawnt Lake Palaeo-Ice Stream Bed: A Signature of Ice Stream Shut-down?

The beds of active ice streams in Greenland and Antarctica are largely inaccessible, hindering a full understanding of the processes that initiate, sustain and inhibit fast ice flow in ice sheets. Detailed mapping of the glacial geomorphology of palaeo-ice stream tracks is, therefore, a valuable tool for exploring the basal processes that control their behaviour. In this paper we present a map that shows detailed glacial geomorphology from a part of the Dubawnt Lake Palaeo-Ice Stream bed on the north-western Canadian Shield (Northwest Territories), which operated at the end of the last glacial cycle. The map (centred on 63 degrees 55 '' 42'N, 102 degrees 29 '' 11'W, approximate scale 1:90,000) was compiled from digital Landsat Enhanced Thematic Mapper Plus satellite imagery and digital and hard-copy stereo-aerial photographs. The ice stream bed is dominated by parallel mega-scale glacial lineations (MGSL), whose lengths exceed several kilometres but the map also reveals that they have, in places, been superimposed with transverse ridges known as ribbed moraines. The ribbed moraines lie on top of the MSGL and appear to have segmented the individual lineaments. This indicates that formation of the ribbed moraines post-date the formation of the MSGL. The presence of ribbed moraine in the onset zone of another palaeo-ice stream has been linked to oscillations between cold and warm-based ice and/or a patchwork of cold-based areas which led to acceleration and deceleration of ice velocity. Our hypothesis is that the ribbed moraines on the Dubawnt Lake Ice Stream bed are a manifestation of the process that led to ice stream shut-down and may be associated with the process of basal freeze-on. The precise formation of ribbed moraines, however, remains open to debate and field observation of their structure will provide valuable data for formal testing of models of their formation.

Subglacial bedform evidence for a major palaeo-ice stream and its retreat phases in Amundsen Gulf, Canadian Arctic Archipelago

Ascertaining the location of palaeo-ice streams is crucial in order to produce accurate reconstructions of palaeo-ice sheets and examine interactions with the ocean-climate system. This paper reports evidence for a major ice stream in Amundsen Gulf, Canadian Arctic Archipelago. Mapping from satellite imagery (Landsat ETM+) and digital elevation models, including bathymetric data, is used to reconstruct flow-patterns on southwestern Victoria Island and the adjacent mainland (Nunavut and Northwest Territories). Several flow-sets indicative of ice streaming are found feeding into the marine trough and cross-cutting relationships between these flow-sets (and utilising previously published radiocarbon dates) reveal several phases of ice stream activity centred in Amundsen Gulf and Dolphin and Union Strait. A large erosional footprint on the continental shelf indicates that the ice stream (ca. 1000 km long and ca. 150 km wide) filled Amundsen Gulf, probably at the Last Glacial Maximum. Subsequent to this, the ice stream reorganised as the margin retreated back along the marine trough, eventually splitting into two separate low-gradient lobes in Prince Albert Sound and Dolphin and Union Strait. The location of this major ice stream holds important implications for ice sheet-ocean interactions and specifically, the development of Arctic Ocean ice shelves and the delivery of icebergs into the western Arctic Ocean during the late Pleistocene. Copyright (C) 2006 John Wiley & Sons, Ltd.