Temporal Mining for Distributed Systems

Many systems and applications are continuously producing events. These events are used to record the status of the system and trace the behaviors of the systems. By examining these events, system administrators can check the potential problems of these systems. If the temporal dynamics of the systems are further investigated, the underlying patterns can be discovered. The uncovered knowledge can be leveraged to predict the future system behaviors or to mitigate the potential risks of the systems. Moreover, the system administrators can utilize the temporal patterns to set up event management rules to make the system more intelligent. With the popularity of data mining techniques in recent years, these events grad- ually become more and more useful. Despite the recent advances of the data mining techniques, the application to system event mining is still in a rudimentary stage. Most of works are still focusing on episodes mining or frequent pattern discovering. These methods are unable to provide a brief yet comprehensible summary to reveal the valuable information from the high level perspective. Moreover, these methods provide little actionable knowledge to help the system administrators to better man- age the systems. To better make use of the recorded events, more practical techniques are required. From the perspective of data mining, three correlated directions are considered to be helpful for system management: (1) Provide concise yet comprehensive summaries about the running status of the systems; (2) Make the systems more intelligence and autonomous; (3) Effectively detect the abnormal behaviors of the systems. Due to the richness of the event logs, all these directions can be solved in the data-driven manner. And in this way, the robustness of the systems can be enhanced and the goal of autonomous management can be approached. This dissertation mainly focuses on the foregoing directions that leverage tem- poral mining techniques to facilitate system management. More specifically, three concrete topics will be discussed, including event, resource demand prediction, and streaming anomaly detection. Besides the theoretic contributions, the experimental evaluation will also be presented to demonstrate the effectiveness and efficacy of the corresponding solutions.

Annotated record and chemical composition of ferromanganese concretions and crusts sampled from the lakes and streams of Norway

The main objective of the project was to develop a geochemical method for exploration of ores associated with granitic rocks. Fe and Mn oxidates were sampled in streambeds and lakes from 129 localities in Southeastern Norway. 65 of these localities are situated in the northern Oslo Graben. The samples were examined mineralogically and chemically by a variety of methods. Geochemical maps of the element content in oxidates show regional distribution patterns for several elements. Sampling and analysis of oxidates can be used in exploration for mineralizations such as the Skrukkelia Mo-deposit in the northern Oslo Graben. New anomalies (especially for Zn and W) have been detected. Appendix I contains a description of samples, chemical and mineralogical determinations performed on the samples, backscattered electron image-, X-ray image- and scanning electron image pictures of the oxidate preparates. Appendix II contains spectral plots, point analysis with the microprobe, X-ray diffractograms, analytical results, correlation coefficient matrix, scatterplots, frequency distributions and information on data storage. Appendix III containS maps of the element content in oxidates.

Macroinvertebrate abundance in tropical Andean streams, Ecuador

Deforestation in the tropical Andes is affecting ecological conditions of streams, and determination of how much forest should be retained is a pressing task for conservation, restoration and management strategies. We calculated and analyzed eight benthic metrics (structural, compositional and water quality indices) and a physical-chemical composite index with gradients of vegetation cover to assess the effects of deforestation on macroinvertebrate communities and water quality of 23 streams in southern Ecuadorian Andes. Using a geographical information system (GIS), we quantified vegetation cover at three spatial scales: the entire catchment, the riparian buffer of 30 m width extending the entire stream length, and the local scale defined for a stream reach of 100 m in length and similar buffer width. Macroinvertebrate and water quality metrics had the strongest relationships with vegetation cover at catchment and riparian scales, while vegetation cover did not show any association with the macroinvertebrate metrics at local scale. At catchment scale, the water quality metrics indicate that ecological condition of Andean streams is good when vegetation cover is over 70%. Further, macroinvertebrate community assemblages were more diverse and related in catchments largely covered by native vegetation (>70%). Overall, our results suggest that retaining an important quantity of native vegetation cover within the catchments and a linkage between headwater and riparian forests help to maintain and improve stream biodiversity and water quality in Andean streams affected by deforestation. Also, this research proposes that a strong regulation focused to the management of riparian buffers can be successful when decision making is addressed to conservation/restoration of Andean catchments.

Macroinvertebrate abundance in montane tropical streams of varying land-use, Ecuador

Despite the importance of tropical montane cloud forest streams, studies investigating aquatic communities in these regions are rare and knowledge on the driving factors of community structure is missing. The objectives of this study therefore were to understand how land-use influences habitat structure and macroinvertebrate communities in cloud forest streams of southern Ecuador. We evaluated these relationships in headwater streams with variable land cover, using multivariate statistics to identify relationships between key habitat variables and assemblage structure, and to resolve differences in composition among sites. Results show that shading intensity, substrate type and pH were the environmental parameters most closely related to variation in community composition observed among sites. In addition, macroinvertebrate density and partly diversity was lower in forested sites, possibly because the pH in forested streams lowered to almost 5 during spates. Standard bioindicator metrics were unable to detect the changes in assemblage structure between disturbed and forested streams. In general, our results indicate that tropical montane headwater streams are complex and heterogeneous ecosystems with low invertebrate densities. We also found that some amount of disturbance, i.e. patchy deforestation, can lead at least initially to an increase in macroinvertebrate taxa richness of these streams.

(Tables 1-3) Water chemistry of cloud forest streams at baseflow conditions, Rio San Francisco, Ecuador

We investigated controls on the water chemistry of a South Ecuadorian cloud forest catchment which is partly pristine, and partly converted to extensive pasture. From April 2007 to May 2008 water samples were taken weekly to biweekly at nine different subcatchments, and were screened for differences in electric conductivity, pH, anion, as well as element composition. A principal component analysis was conducted to reduce dimensionality of the data set and define major factors explaining variation in the data. Three main factors were isolated by a subset of 10 elements (Ca2+, Ce, Gd, K+, Mg2+, Na+, Nd, Rb, Sr, Y), explaining around 90% of the data variation. Land-use was the major factor controlling and changing water chemistry of the subcatchments. A second factor was associated with the concentration of rare earth elements in water, presumably highlighting other anthropogenic influences such as gravel excavation or road construction. Around 12% of the variation was explained by the third component, which was defined by the occurrence of Rb and K and represents the influence of vegetation dynamics on element accumulation and wash-out. Comparison of base- and fast flow concentrations led to the assumption that a significant portion of soil water from around 30 cm depth contributes to storm flow, as revealed by increased rare earth element concentrations in fast flow samples. Our findings demonstrate the utility of multi-tracer principal component analysis to study tropical headwater streams, and emphasize the need for effective land management in cloud forest catchments.

Classificazione e selezione di tecniche di visualizzazione per Big Data Analytics

La tesi presenta uno studio della libreria grafica per web D3, sviluppata in javascript, e ne presenta una catalogazione dei grafici implementati e reperibili sul web. Lo scopo è quello di valutare la libreria e studiarne i pregi e difetti per capire se sia opportuno utilizzarla nell'ambito di un progetto Europeo. Per fare questo vengono studiati i metodi di classificazione dei grafici presenti in letteratura e viene esposto e descritto lo stato dell'arte del data visualization. Viene poi descritto il metodo di classificazione proposto dal team di progettazione e catalogata la galleria di grafici presente sul sito della libreria D3. Infine viene presentato e studiato in maniera formale un algoritmo per selezionare un grafico in base alle esigenze dell'utente.

Inventory of thermo-erosional valleys and streams in three ice-rich permafrost lowlands adjacent to the Laptev Sea, with links to maps in ArcGIS format

This dataset provides an inventory of thermo-erosional landforms and streams in three lowland areas underlain by ice-rich permafrost of the Yedoma-type Ice Complex at the Siberian Laptev Sea coast. It consists of two shapefiles per study region: one shapefile for the digitized thermo-erosional landforms and streams, one for the study area extent. Thermo-erosional landforms were manually digitized from topographic maps and satellite data as line features and subsequently analyzed in a Geographic Information System (GIS) using ArcGIS 10.0. The mapping included in particular thermo-erosional gullies and valleys as well as streams and rivers, since development of all of these features potentially involved thermo-erosional processes. For the Cape Mamontov Klyk site, data from Grosse et al. [2006], which had been digitized from 1:100000 topographic map sheets, were clipped to the Ice Complex extent of Cape Mamontov Klyk, which excludes the hill range in the southwest with outcropping bedrock and rocky slope debris, coastal barrens, and a large sandy floodplain area in the southeast. The mapped features (streams, intermittent streams) were then visually compared with panchromatic Landsat-7 ETM+ satellite data (4 August 2000, 15 m spatial resolution) and panchromatic Hexagon data (14 July 1975, 10 m spatial resolution). Smaller valleys and gullies not captured in the maps were subsequently digitized from the satellite data. The criterion for the mapping of linear features as thermo-erosional valleys and gullies was their clear incision into the surface with visible slopes. Thermo-erosional features of the Lena Delta site were mapped on the basis of a Landsat-7 ETM+ image mosaic (2000 and 2001, 30 m ground resolution) [Schneider et al., 2009] and a Hexagon satellite image mosaic (1975, 10 m ground resolution) [G. Grosse, unpublished data] of the Lena River Delta within the extent of the Lena Delta Ice Complex [Morgenstern et al., 2011]. For the Buor Khaya Peninsula, data from Arcos [2012], which had been digitized based on RapidEye satellite data (8 August 2010, 6.5 m ground resolution), were completed for smaller thermo-erosional features using the same RapidEye scene as a mapping basis. The spatial resolution, acquisition date, time of the day, and viewing geometry of the satellite data used may have influenced the identification of thermo-erosional landforms in the images. For Cape Mamontov Klyk and the Lena Delta, thermo-erosional features were digitized using both Hexagon and Landsat data; Hexagon provided higher resolution and Landsat provided the modern extent of features. Allowance of up to decameters was made for the lateral expansion of features between Hexagon and Landsat acquisitions (between 1975 and 2000).

Sentiment-Topic Modelling in Text Mining

Peer reviewed

Sentiment-Topic Modelling in Text Mining

Peer reviewed

Continuous Dissolved Oxygen Measurements and Modelling Metabolism in Peatland Streams

Funding: This work was supported by the following sources of funding: European Research Council ERC (project GA 335910 VEWA) for funding through the VeWa project (DT); Leverhulme Trust for funding through PLATO (RPG-2014-016) (DT). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Sentiment-Topic Modelling in Text Mining

Peer reviewed

Occupant location prediction in smart buildings using association rule mining

Heating, ventilation, air conditioning (HVAC) systems are significant consumers of energy, however building management systems do not typically operate them in accordance with occupant movements. Due to the delayed response of HVAC systems, prediction of occupant locations is necessary to maximize energy efficiency. We present an approach to occupant location prediction based on association rule mining, allowing prediction based on historical occupant locations. Association rule mining is a machine learning technique designed to find any correlations which exist in a given dataset. Occupant location datasets have a number of properties which differentiate them from the market basket datasets that association rule mining was originally designed for. This thesis adapts the approach to suit such datasets, focusing the rule mining process on patterns which are useful for location prediction. This approach, named OccApriori, allows for the prediction of occupants’ next locations as well as their locations further in the future, and can take into account any available data, for example the day of the week, the recent movements of the occupant, and timetable data. By integrating an existing extension of association rule mining into the approach, it is able to make predictions based on general classes of locations as well as specific locations.