Schema Vacuuming in Temporal Databases

Temporal databases facilitate the support of historical information by providing functions for indicating the intervals during which a tuple was applicable (along one or more temporal dimensions). Because data are never deleted, only superceded, temporal databases are inherently append-only resulting, over time, in a large historical sequence of database states. Data vacuuming in temporal databases allows for this sequence to be shortened by strategically, and irrevocably, deleting obsolete data. Schema versioning allows users to maintain a history of database schemata without compromising the semantics of the data or the ability to view data through historical schemata. While the techniques required for data vacuuming in temporal databases have been relatively well covered, the associated area of vacuuming schemata has received less attention. This paper discusses this issue and proposes a mechanism that fits well with existing methods for data vacuuming and schema versioning

Efficient Management of Short-Lived Data

Motivated by the increasing prominence of loosely-coupled systems, such as mobile and sensor networks, which are characterised by intermittent connectivity and volatile data, we study the tagging of data with so-called expiration times. More specifically, when data are inserted into a database, they may be tagged with time values indicating when they expire, i.e., when they are regarded as stale or invalid and thus are no longer considered part of the database. In a number of applications, expiration times are known and can be assigned at insertion time. We present data structures and algorithms for online management of data tagged with expiration times. The algorithms are based on fully functional, persistent treaps, which are a combination of binary search trees with respect to a primary attribute and heaps with respect to a secondary attribute. The primary attribute implements primary keys, and the secondary attribute stores expiration times in a minimum heap, thus keeping a priority queue of tuples to expire. A detailed and comprehensive experimental study demonstrates the well-behavedness and scalability of the approach as well as its efficiency with respect to a number of competitors.Comment: switched to TimeCenter latex styl

The relevance of the evolution of experimental studies for the interpretation and evaluation of some trace physical evidence

In order for trace evidence to have a high evidential value, experimental studies which mimic the forensic reality are of fundamental importance. Such primary level experimentation is crucial to establish a coherent body of theory concerning the generation, transfer and persistence of different forms of trace physical evidence. We contend that the forensic context, at whatever scale, will be specific to each individual forensic case and this context in which a crime takes place will influence the properties of trace evidence. it will, therefore, be necessary in many forensic cases to undertake secondary level experimental studies that incorporate specific variables pertinent to a particular case and supplement the established theory presented in the published literature. Such studies enable a better understanding of the specific forensic context and thus allow More accurate collection, analysis and interpretation of the trace physical evidence to be achieved. This paper presents two cases where the findings of secondary level experimental studies undertaken to address specific issues particular to two forensic investigations proved to be important. Specific pre-, syn- and post-forensic event factors were incorporated into the experimental design and proved to be invaluable in the recovery, analysis and in achieving accurate interpretations of both soil evidence from footwear and glass trace evidence from a broken window.These Studies demonstrate that a fuller understanding of the specific context within which trace physical evidence is generated and subsequently collected, as well as an understanding of the behaviour of certain forms of trace physical evidence under specific conditions, can add evidentiary weight to the analysis and interpretation of that evidence and thus help a court with greater certainty where resources (time and cost) permit

Big Data technology

Big Data must be processed with advanced collection and analysis tools, based on predetermined algorithms, in order to obtain relevant information. Algorithms must also take into account invisible aspects for direct perceptions. Big Data issues is multi-layered. A distributed parallel architecture distributes data on multiple servers (parallel execution environments) thus dramatically improving data processing speeds. Big Data provides an infrastructure that allows for highlighting uncertainties, performance, and availability of components. DOI: 10.13140/RG.2.2.12784.0000

The validity of an accelerometer-based activity monitoring system and the consistency of locomotive activity of community-living older adults

BACKGROUND: The amount and intensity of people's activities are related to latent chronic diseases and the aging process. Accurate information about people's patterns of activity in their natural environments would go a long way toward understanding the relationship between types/levels of activity and health. Unlike the commercially available activity monitors, an algorithm developed at Boston University utilizes frequency (cycles/second) to identify overground gait and pedaling. These studies evaluate the validity of this system in a real-life environment and then investigate people's locomotive behavior across weekdays of the same week. METHODS: Wearing the monitoring system developed at BU on their right ankles, 16 older adults performed a battery of functional locomotive activities continuously in a residential setting, while being video recorded for reference. For the validity algorithm output regarding gait and pedaling variables was statistically compared to the video analysis of the same using the intraclass correlation coefficient (ICC). To investigate the consistency of locomotor behavior across weekdays of the same week, 227 older adults wore the monitoring system under study on their right ankles continuously for a week. Daily gait and pedaling values were correlated across weekdays of the same week also using ICCs. An investigation into the differences in gait variability for the average of 3 weekdays according to the subgroups; age, gender, and BMI was conducted on this sample using the Wilcoxon Signed Ranks test. RESULTS: Three of the four gait validity ICCs were significant (p ≤ 0.019) ranging from 0.267 to 0.778. All pedaling validity variables had ICCs ≥ 0.993 The locomotive consistency study found all 6 daily gait variables significantly (p < 0.001) correlated across 3 weekdays, ranging from 0.534 to 0.914. Three of four ICCs for pedaling consistency variables were significant (p ≤ 0.029) ranging from 0.277 to 0.838. CONCLUSIONS: This study's validity results support this monitoring system's gait and pedaling identification approach. There is also evidence to suggest how the system could improve its real-life locomotive detection validity and potentially diversify its applications. Additionally, based on this dissertation's results, some of people's daily locomotive behaviors remain relatively constant over weekdays during the same week

An Abstract Formal Basis for Digital Crowds

Crowdsourcing, together with its related approaches, has become very popular in recent years. All crowdsourcing processes involve the participation of a digital crowd, a large number of people that access a single Internet platform or shared service. In this paper we explore the possibility of applying formal methods, typically used for the verification of software and hardware systems, in analysing the behaviour of a digital crowd. More precisely, we provide a formal description language for specifying digital crowds. We represent digital crowds in which the agents do not directly communicate with each other. We further show how this specification can provide the basis for sophisticated formal methods, in particular formal verification.Comment: 32 pages, 4 figure

Impacts of Subway System Modifications on Air Quality in Subway Platforms and Trains.

Subway PM₂.₅ can be substantially sourced from the operation of the system itself. Improvements in subway air quality may be possible by examining the potential to reduce these emissions. To this end, PM₂.₅ was measured on the trains and station platforms of the Toronto subway system. A comparison with previously published data for this system reveals significant changes in below ground platform PM₂.₅. A reduction of nearly one-third (ratio (95% CI): 0.69 (0.63, 0.75)) in PM₂.₅ from 2011 to 2018 appears to have resulted from a complete modernization of the rolling stock on one subway line. In contrast, below ground platform PM₂.₅ for another line increased by a factor of 1.48 (95% CI; 1.42, 1.56). This increase may be related to an increase in emergency brake applications, the resolution of which coincided with a large decrease in PM₂.₅ concentrations on that line. Finally, platform PM₂.₅ in two newly opened stations attained, within one year of operation, typical concentrations of the neighboring platforms installed in 1963. Combined, these findings suggest that the production of platform PM₂.₅ is localized and hence largely freshly emitted. Further, PM₂.₅ changed across this subway system due to changes in its operation and rolling stock. Thus, similar interventions applied intentionally may prove to be equally effective in reducing PM₂.₅. Moreover, establishing a network of platform PM₂.₅ monitors is recommended to monitor ongoing improvements and identify impacts of future system changes on subway air quality. This would result in a better understanding of the relationship between the operations and air quality of subways