An epistemic event -based correlation scheme for pervasive network management

Computer networks produce tremendous amounts of event-based data that can be collected and managed to support an increasing number of new classes of pervasive applications. Examples of such applications are network monitoring and crisis management. Although the problem of distributed event-based management has been addressed in the non-pervasive settings such as the Internet, the domain of pervasive networks has its own characteristics that make these results non-applicable. Many of these applications are based on time-series data that possess the form of time-ordered series of events. Such applications also embody the need to handle large volumes of unexpected events, often modified on-the-fly, containing conflicting information, and dealing with rapidly changing contexts while producing results with low-latency. Correlating events across contextual dimensions holds the key to expanding the capabilities and improving the performance of these applications. This dissertation addresses this critical challenge. It establishes an effective scheme for complex-event semantic correlation. The scheme examines epistemic uncertainty in computer networks by fusing event synchronization concepts with belief theory. Because of the distributed nature of the event detection, time-delays are considered. Events are no longer instantaneous, but duration is associated with them. Existing algorithms for synchronizing time are split into two classes, one of which is asserted to provide a faster means for converging time and hence better suited for pervasive network management. Besides the temporal dimension, the scheme considers imprecision and uncertainty when an event is detected. A belief value is therefore associated with the semantics and the detection of composite events. This belief value is generated by a consensus among participating entities in a computer network. The scheme taps into in-network processing capabilities of pervasive computer networks and can withstand missing or conflicting information gathered from multiple participating entities. Thus, this dissertation advances knowledge in the field of network management by facilitating the full utilization of characteristics offered by pervasive, distributed and wireless technologies in contemporary and future computer networks.

Tree Mortality following Prescribed Fire and a Storm Surge Event in Slash Pine (Pinus elliottii var. densa) Forests in the Florida Keys, USA

In fire-dependent forests, managers are interested in predicting the consequences of prescribed burning on postfire tree mortality. We examined the effects of prescribed fire on tree mortality in Florida Keys pine forests, using a factorial design with understory type, season, and year of burn as factors. We also used logistic regression to model the effects of burn season, fire severity, and tree dimensions on individual tree mortality. Despite limited statistical power due to problems in carrying out the full suite of planned experimental burns, associations with tree and fire variables were observed. Post-fire pine tree mortality was negatively correlated with tree size and positively correlated with char height and percent crown scorch. Unlike post-fire mortality, tree mortality associated with storm surge from Hurricane Wilma was greater in the large size classes. Due to their influence on population structure and fuel dynamics, the size-selective mortality patterns following fire and storm surge have practical importance for using fire as a management tool in Florida Keys pinelands in the future, particularly when the threats to their continued existence from tropical storms and sea level rise are expected to increase.

Effects of an extreme temperature event on the behavior and age structure of an estuarine top predator, Carcharhinus leucas

The frequency of extreme environmental events is predicted to increase in the future. Understanding the short- and long-term impacts of these extreme events on large-bodied predators will provide insight into the spatial and temporal scales at which acute environmental disturbances in top-down processes may persist within and across ecosystems. Here, we use long-term studies of movements and age structure of an estuarine top predator—juvenile bull sharks Carcharhinus leucas—to identify the effects of an extreme ‘cold snap’ from 2 to 13 January 2010 over short (weeks) to intermediate (months) time scales. Juvenile bull sharks are typically year-round residents of the Shark River Estuary until they reach 3 to 5 yr of age. However, acoustic telemetry revealed that almost all sharks either permanently left the system or died during the cold snap. For 116 d after the cold snap, no sharks were detected in the system with telemetry or captured during longline sampling. Once sharks returned, both the size structure and abundance of the individuals present in the nursery had changed considerably. During 2010, individual longlines were 70% less likely to capture any sharks, and catch rates on successful longlines were 40% lower than during 2006−2009. Also, all sharks caught after the cold snap were young-of-the-year or neonates, suggesting that the majority of sharks in the estuary were new recruits and several cohorts had been largely lost from the nursery. The longer-term impacts of this change in bull shark abundance to the trophic dynamics of the estuary and the importance of episodic disturbances to bull shark population dynamics will require continued monitoring, but are of considerable interest because of the ecological roles of bull sharks within coastal estuaries and oceans.

Event mining for system and service management

Modern IT infrastructures are constructed by large scale computing systems and administered by IT service providers. Manually maintaining such large computing systems is costly and inefficient. Service providers often seek automatic or semi-automatic methodologies of detecting and resolving system issues to improve their service quality and efficiency. This dissertation investigates several data-driven approaches for assisting service providers in achieving this goal. The detailed problems studied by these approaches can be categorized into the three aspects in the service workflow: 1) preprocessing raw textual system logs to structural events; 2) refining monitoring configurations for eliminating false positives and false negatives; 3) improving the efficiency of system diagnosis on detected alerts. Solving these problems usually requires a huge amount of domain knowledge about the particular computing systems. The approaches investigated by this dissertation are developed based on event mining algorithms, which are able to automatically derive part of that knowledge from the historical system logs, events and tickets. ^ In particular, two textual clustering algorithms are developed for converting raw textual logs into system events. For refining the monitoring configuration, a rule based alert prediction algorithm is proposed for eliminating false alerts (false positives) without losing any real alert and a textual classification method is applied to identify the missing alerts (false negatives) from manual incident tickets. For system diagnosis, this dissertation presents an efficient algorithm for discovering the temporal dependencies between system events with corresponding time lags, which can help the administrators to determine the redundancies of deployed monitoring situations and dependencies of system components. To improve the efficiency of incident ticket resolving, several KNN-based algorithms that recommend relevant historical tickets with resolutions for incoming tickets are investigated. Finally, this dissertation offers a novel algorithm for searching similar textual event segments over large system logs that assists administrators to locate similar system behaviors in the logs. Extensive empirical evaluation on system logs, events and tickets from real IT infrastructures demonstrates the effectiveness and efficiency of the proposed approaches.^

FIU Hispanic Scholarship Fund Event

Florida International University hosts an information session for the Hispanic Scholarship's Fund Steps for Success program. The program assists students and parents by providing workshops on financial aid and the college application process. January 21, 2012 at the Graham Center Ballroom, Modesto Maidique Campus, Florida International University

Text analytics of social media: Sentiment analysis, event detection and summarization

In the last decade, large numbers of social media services have emerged and been widely used in people's daily life as important information sharing and acquisition tools. With a substantial amount of user-contributed text data on social media, it becomes a necessity to develop methods and tools for text analysis for this emerging data, in order to better utilize it to deliver meaningful information to users. ^ Previous work on text analytics in last several decades is mainly focused on traditional types of text like emails, news and academic literatures, and several critical issues to text data on social media have not been well explored: 1) how to detect sentiment from text on social media; 2) how to make use of social media's real-time nature; 3) how to address information overload for flexible information needs. ^ In this dissertation, we focus on these three problems. First, to detect sentiment of text on social media, we propose a non-negative matrix tri-factorization (tri-NMF) based dual active supervision method to minimize human labeling efforts for the new type of data. Second, to make use of social media's real-time nature, we propose approaches to detect events from text streams on social media. Third, to address information overload for flexible information needs, we propose two summarization framework, dominating set based summarization framework and learning-to-rank based summarization framework. The dominating set based summarization framework can be applied for different types of summarization problems, while the learning-to-rank based summarization framework helps utilize the existing training data to guild the new summarization tasks. In addition, we integrate these techneques in an application study of event summarization for sports games as an example of how to better utilize social media data. ^

Text Analytics of Social Media: Sentiment Analysis, Event Detection and Summarization

In the last decade, large numbers of social media services have emerged and been widely used in people's daily life as important information sharing and acquisition tools. With a substantial amount of user-contributed text data on social media, it becomes a necessity to develop methods and tools for text analysis for this emerging data, in order to better utilize it to deliver meaningful information to users. Previous work on text analytics in last several decades is mainly focused on traditional types of text like emails, news and academic literatures, and several critical issues to text data on social media have not been well explored: 1) how to detect sentiment from text on social media; 2) how to make use of social media's real-time nature; 3) how to address information overload for flexible information needs. In this dissertation, we focus on these three problems. First, to detect sentiment of text on social media, we propose a non-negative matrix tri-factorization (tri-NMF) based dual active supervision method to minimize human labeling efforts for the new type of data. Second, to make use of social media's real-time nature, we propose approaches to detect events from text streams on social media. Third, to address information overload for flexible information needs, we propose two summarization framework, dominating set based summarization framework and learning-to-rank based summarization framework. The dominating set based summarization framework can be applied for different types of summarization problems, while the learning-to-rank based summarization framework helps utilize the existing training data to guild the new summarization tasks. In addition, we integrate these techneques in an application study of event summarization for sports games as an example of how to better utilize social media data.

An Epistemic Event-based Correlation Scheme for Pervasive Network Management

Computer networks produce tremendous amounts of event-based data that can be collected and managed to support an increasing number of new classes of pervasive applications. Examples of such applications are network monitoring and crisis management. Although the problem of distributed event-based management has been addressed in the non-pervasive settings such as the Internet, the domain of pervasive networks has its own characteristics that make these results non-applicable. Many of these applications are based on time-series data that possess the form of time-ordered series of events. Such applications also embody the need to handle large volumes of unexpected events, often modified on-the-fly, containing conflicting information, and dealing with rapidly changing contexts while producing results with low-latency. Correlating events across contextual dimensions holds the key to expanding the capabilities and improving the performance of these applications. This dissertation addresses this critical challenge. It establishes an effective scheme for complex-event semantic correlation. The scheme examines epistemic uncertainty in computer networks by fusing event synchronization concepts with belief theory. Because of the distributed nature of the event detection, time-delays are considered. Events are no longer instantaneous, but duration is associated with them. Existing algorithms for synchronizing time are split into two classes, one of which is asserted to provide a faster means for converging time and hence better suited for pervasive network management. Besides the temporal dimension, the scheme considers imprecision and uncertainty when an event is detected. A belief value is therefore associated with the semantics and the detection of composite events. This belief value is generated by a consensus among participating entities in a computer network. The scheme taps into in-network processing capabilities of pervasive computer networks and can withstand missing or conflicting information gathered from multiple participating entities. Thus, this dissertation advances knowledge in the field of network management by facilitating the full utilization of characteristics offered by pervasive, distributed and wireless technologies in contemporary and future computer networks.

Transportation Outreach Planner

The Federal Highway Administration (FHWA) September 2003 Certification Report recommended that the Miami-Dade Metropolitan Planning Organization (MPO) incorporate 'Sociocultural Effect' features in its planning process to ensure community values and concerns receive proper attention throughout the entire transportation development process. In response, the Miami-Dade MPO created the Community Characteristics Project (CCP) in order to review the social, economic, and geographic characteristics of an area before public involvement (PI) efforts are initiated. In 2010 the Broward and Palm Beach MPOs joined the program, and the CCP was renamed the "Transportation Outreach Planner".

CRI Event/Project List: 7/1/2012 thru 6/30/2013

This document is a blank template that was to be filled with 2012-13 Cuban Research Institute event and project information.

Lower Aptian Comparative Stratigraphy of the Basco-Cantabrian Region (Spain) and Eastern Cordillera (Colombia): implications for local factors in the depositional record of Oceanic Anoxic Event 1a (OAE-1a)

An important episode of carbon sequestration, Oceanic Anoxic Event 1a (OAE-1a), characterizes the Lower Aptian worldwide, and is mostly known from deeper-water settings. The present work of two Lower Aptian deposits, Madotz (N Spain) and Curití Quarry (Colombia), is a multiproxy study that includes fossil assemblages, microfacies, X-ray diffraction bulk and clay mineralogy, elemental analyses (major, minor, trace elements), Rock-Eval pyrolysis, biomarkers, inorganic and organic carbon content, and stable carbon isotopes. The results provide baseline evidence of the local and global controlling environmental factors influencing OAE-1a in shallow-water settings. The data also improve our general understanding of the conditions under which organic-carbon-rich deposits accumulate. The sequence at Madotz includes four intervals (Unit 1; Subunits 2a, 2b and 2c) that overlap the times prior to, during and after the occurrence of OAE-1a. The Lower Unit 1(3m thick) is essentially siliciclastic, and Subunit 2a (20m) contains Urgonian carbonate facies that document abruptly changing platform conditions prior to OAE-1a. Subunit 2b (24.4 m) is a mixed carbonate-siliciclastic facies with orbitolinid-rich levels that coincides with OAE-1a δ13C stages C4-C6, and is coeval with the upper part of the Deshayesites forbesi ammonite zone. Levels with pyrite and the highest TOC values (0.4-0.97%), interpreted as accumulating under suboxic conditions, and are restricted to δ13C stages C4 and C5. The best development of the suboxic facies is at the level representing the peak of the transgression. Subunit 2c, within δ13C stage C7, shows a return of the Urgonian facies. The 23.35-m section at Curití includes a 6.3-m interval at the base of the Paja Formation dominated by organic-rich marlstones and shales lacking benthic fossils and bioturbation, with TOC values as high as 8.84%. The interval overlies a level containing reworked and phosphatized assemblages of middle Barremian to lowest Aptian ammonites. The range of values and the overall pattern of the δ13Corg (-22.05‰ to -20.47‰) in the 6.3m-interval is comparable with Lower Aptian δ13C stage C7. Thus, conditions of oxygen depletion at this site also occurred after Oceanic Anoxic Event-1a, which developed between carbon isotope stages C3 and C6. Both sites, Madotz and Curití, attest to the importance of terrigenous and nutrient fluxes in increasing OM productivity that led to episodic oxygen deficiency.