South Beach Wine and Food Festival - Why Participate?

This paper studies why restaurants, wineries, and other exhibiters participate in Wine & Food festivals. We hypothesized [hat the purpose was to acquire new customers thru promotional involvement in the festival. A secondary outcome was to ascertain if there were differences in motivation between the three groups. A survey was conducted of participating companies in one of the largest Food & Wine festivals. We found differences in what motivated winery participants from restaurants or other exhibitors. A discussion of these differences and how festival organizers may aid participants in achieving their goals is presented.

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.

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.

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.^

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.

Knowledge assisted data management and retrieval in multimedia database systems

With the proliferation of multimedia data and ever-growing requests for multimedia applications, there is an increasing need for efficient and effective indexing, storage and retrieval of multimedia data, such as graphics, images, animation, video, audio and text. Due to the special characteristics of the multimedia data, the Multimedia Database management Systems (MMDBMSs) have emerged and attracted great research attention in recent years. Though much research effort has been devoted to this area, it is still far from maturity and there exist many open issues. In this dissertation, with the focus of addressing three of the essential challenges in developing the MMDBMS, namely, semantic gap, perception subjectivity and data organization, a systematic and integrated framework is proposed with video database and image database serving as the testbed. In particular, the framework addresses these challenges separately yet coherently from three main aspects of a MMDBMS: multimedia data representation, indexing and retrieval. In terms of multimedia data representation, the key to address the semantic gap issue is to intelligently and automatically model the mid-level representation and/or semi-semantic descriptors besides the extraction of the low-level media features. The data organization challenge is mainly addressed by the aspect of media indexing where various levels of indexing are required to support the diverse query requirements. In particular, the focus of this study is to facilitate the high-level video indexing by proposing a multimodal event mining framework associated with temporal knowledge discovery approaches. With respect to the perception subjectivity issue, advanced techniques are proposed to support users' interaction and to effectively model users' perception from the feedback at both the image-level and object-level.

DIMUSE: An integrated framework for distributed multimedia system with database management and security support

With the recent explosion in the complexity and amount of digital multimedia data, there has been a huge impact on the operations of various organizations in distinct areas, such as government services, education, medical care, business, entertainment, etc. To satisfy the growing demand of multimedia data management systems, an integrated framework called DIMUSE is proposed and deployed for distributed multimedia applications to offer a full scope of multimedia related tools and provide appealing experiences for the users. This research mainly focuses on video database modeling and retrieval by addressing a set of core challenges. First, a comprehensive multimedia database modeling mechanism called Hierarchical Markov Model Mediator (HMMM) is proposed to model high dimensional media data including video objects, low-level visual/audio features, as well as historical access patterns and frequencies. The associated retrieval and ranking algorithms are designed to support not only the general queries, but also the complicated temporal event pattern queries. Second, system training and learning methodologies are incorporated such that user interests are mined efficiently to improve the retrieval performance. Third, video clustering techniques are proposed to continuously increase the searching speed and accuracy by architecting a more efficient multimedia database structure. A distributed video management and retrieval system is designed and implemented to demonstrate the overall performance. The proposed approach is further customized for a mobile-based video retrieval system to solve the perception subjectivity issue by considering individual user's profile. Moreover, to deal with security and privacy issues and concerns in distributed multimedia applications, DIMUSE also incorporates a practical framework called SMARXO, which supports multilevel multimedia security control. SMARXO efficiently combines role-based access control (RBAC), XML and object-relational database management system (ORDBMS) to achieve the target of proficient security control. A distributed multimedia management system named DMMManager (Distributed MultiMedia Manager) is developed with the proposed framework DEMUR; to support multimedia capturing, analysis, retrieval, authoring and presentation in one single framework.

Relating precipitation and water management to nutrient concentrations in the oligotrophic ‘‘upside-down’’ estuaries of the Florida Everglades

We present 8 yr of long-term water quality, climatological, and water management data for 17 locations in Everglades National Park, Florida. Total phosphorus (P) concentration data from freshwater sites (typically ,0.25 mmol L21, or 8 mg L21) indicate the oligotrophic, P-limited nature of this large freshwater–estuarine landscape. Total P concentrations at estuarine sites near the Gulf of Mexico (average ø0.5 m mol L21) demonstrate the marine source for this limiting nutrient. This ‘‘upside down’’ phenomenon, with the limiting nutrient supplied by the ocean and not the land, is a defining characteristic of the Everglade landscape. We present a conceptual model of how the seasonality of precipitation and the management of canal water inputs control the marine P supply, and we hypothesize that seasonal variability in water residence time controls water quality through internal biogeochemical processing. Low freshwater inflows during the dry season increase estuarine residence times, enabling local processes to control nutrient availability and water quality. El Nin˜o–Southern Oscillation (ENSO) events tend to mute the seasonality of rainfall without altering total annual precipitation inputs. The Nin˜o3 ENSO index (which indicates an ENSO event when positive and a La Nin˜a event when negative) was positively correlated with both annual rainfall and the ratio of dry season to wet season precipitation. This ENSO-driven disruption in seasonal rainfall patterns affected salinity patterns and tended to reduce marine inputs of P to Everglades estuaries. ENSO events also decreased dry season residence times, reducing the importance of estuarine nutrient processing. The combination of variable water management activities and interannual differences in precipitation patterns has a strong influence on nutrient and salinity patterns in Everglades estuaries.

Disaster Risk Management Disparity in the Caribbean: Evidence from Barbados, Dominican Republic, Jamaica and Trinidad and Tobago

This study on risk and disaster management capacities of four Caribbean countries: Barbados, the Dominican Republic, Jamaica, and Trinidad and Tobago, examines three main dimensions: 1) the impact of natural disasters from 1900 to 2010 (number of events, number of people killed, total number affected, and damage in US$); 2) institutional assessments of disaster risk management disparity; and 3) the 2010 Inter-American Bank for Development (IADB) Disaster Risk and Risk Management indicators for the countries under study. The results show high consistency among the different sources examined, pointing out the need to extend the IADB measurements to the rest of the Caribbean countries. Indexes and indicators constitute a comparison measure vis-à-vis existing benchmarks in order to anticipate a capacity to deal with adverse events and their consequences; however, the indexes and indicators could only be tested against the occurrence of a real event. Therefore, the need exists to establish a sustainable and comprehensive evaluation system after important disasters to assess a country‘s performance, verify the indicators, and gain feedback on measurement systems and methodologies. There is diversity in emergency and preparedness for disasters in the four countries under study. The nature of the event (hurricanes, earthquakes, floods, and seismic activity), especially its frequency and the intensity of the damage experienced, is related to how each has designed its risk and disaster management policies and programs to face natural disasters. Vulnerabilities to disaster risks have been increasing, among other factors, because of uncontrolled urbanization, demographic density and poverty increase, social and economic marginalization, and lack of building code enforcement. The four countries under study have shown improvements in risk management capabilities, yet they are far from being completed prepared. Barbados‘ risk management performance is superior, in comparison, to the majority of the countries of the region. However, is still far in achieving high performance levels and sustainability in risk management, primarily when it has the highest gap between potential macroeconomic and financial losses and the ability to face them. The Dominican Republic has shown steady risk performance up to 2008, but two remaining areas for improvement are hazard monitoring and early warning systems. Jamaica has made uneven advances between 1990 and 2008, requiring significant improvements to achieve high performance levels and sustainability in risk management, as well as macroeconomic mitigation infrastructure. Trinidad and Tobago has the lowest risk management score of the 15 countries in the Latin American and Caribbean region as assessed by the IADB study in 2010, yet it has experienced an important vulnerability reduction. In sum, the results confirmed the high disaster risk management disparity in the Caribbean region.

Disturbance and the rising tide: the challenge of biodiversity management on low-island ecosystems

Sea-level rise presents an imminent threat to freshwater-dependent ecosystems on small oceanic islands, which often harbor rare and endemic taxa. Conservation of these assemblages is complicated by feedbacks between sea level and recurring pulse disturbances (eg hurricanes, fire). Once sea level reaches a critical level, the transition from a landscape characterized by mesophytic upland forests and freshwater wetlands to one dominated by mangroves can occur suddenly, following a single storm-surge event. We document such a trajectory, unfolding today in the Florida Keys. With sea level projected to rise substantially during the next century, ex-situ actions may be needed to conserve individual species of special concern. However, within existing public conservation units, managers have a responsibility to conserve extant biodiversity. We propose a strategy that combines the identification and intensive management of the most defensible core sites within a broader reserve system, in which refugia for biota facing local extirpation may be sought.

Disater Risk Management Disparity in the Caribbean: Evidence from Barbados, Dominican Republic, Jamaica and Trinidad and Tobago

This study on risk and disaster management capacities of four Caribbean countries: Barbados, the Dominican Republic, Jamaica, and Trinidad and Tobago, examines three main dimensions: 1) the impact of natural disasters from 1900 to 2010 (number of events, number of people killed, total number affected, and damage in US$); 2) institutional assessments of disaster risk management disparity; and 3) the 2010 Inter-American Bank for Development (IADB) Disaster Risk and Risk Management indicators for the countries under study. The results show high consistency among the different sources examined, pointing out the need to extend the IADB measurements to the rest of the Caribbean countries. Indexes and indicators constitute a comparison measure vis-à-vis existing benchmarks in order to anticipate a capacity to deal with adverse events and their consequences; however, the indexes and indicators could only be tested against the occurrence of a real event. Therefore, the need exists to establish a sustainable and comprehensive evaluation system after important disasters to assess a country’s performance, verify the indicators, and gain feedback on measurement systems and methodologies. There is diversity in emergency and preparedness for disasters in the four countries under study. The nature of the event (hurricanes, earthquakes, floods, and seismic activity), especially its frequency and the intensity of the damage experienced, is related to how each has designed its risk and disaster management policies and programs to face natural disasters. Vulnerabilities to disaster risks have been increasing, among other factors, because of uncontrolled urbanization, demographic density and poverty increase, social and economic marginalization, and lack of building code enforcement. The four countries under study have shown improvements in risk management capabilities, yet they are far from being completed prepared. Barbados’ risk management performance is superior, in comparison, to the majority of the countries of the region. However, is still far in achieving high performance levels and sustainability in risk management, primarily when it has the highest gap between potential macroeconomic and financial losses and the ability to face them. The Dominican Republic has shown steady risk performance up to 2008, but two remaining areas for improvement are hazard monitoring and early warning systems. Jamaica has made uneven advances between 1990 and 2008, requiring significant improvements to achieve high performance levels and sustainability in risk management, as well as macroeconomic mitigation infrastructure. Trinidad and Tobago has the lowest risk management score of the 15 countries in the Latin American and Caribbean region as assessed by the IADB study in 2010, yet it has experienced an important vulnerability reduction. In sum, the results confirmed the high disaster risk management disparity in the Caribbean region.

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.