A new and efficient algorithm to look for periodic patterns on spatio-temporal databases

Big Data is a generic term that involves the storing and processing of a large amount of data. This large amount of data has been promoted by technologies such as mobile applications, Internet of Things (IoT), and Geographic Information Systems (GIS). An example of GIS is a Spatio-Temporal Database (STDB). A complex problem to address in terms of processing time is pattern searching on STDB. Nowadays, high information processing capacity is available everywhere. Nevertheless, the pattern searching problem on STDB using traditional Data Mining techniques is complex because the data incorporate the temporal aspect. Traditional techniques of pattern searching, such as time series, do not incorporate the spatial aspect. For this reason, traditional algorithms based on association rules must be adapted to find these patterns. Most of the algorithms take exponential processing times. In this paper, a new efficient algorithm (named Minus-F1) to look for periodic patterns on STDB is presented. Our algorithm is compared with Apriori, Max-Subpattern, and PPA algorithms on synthetic and real STDB. Additionally, the computational complexities for each algorithm in the worst cases are presented. Empirical results show that Minus-F1 is not only more efficient than Apriori, Max-Subpattern, and PAA, but also it presents a polynomial behavior.

A systematic review of smartphone-based human activity recognition methods for health research

Smartphones are now nearly ubiquitous; their numerous built-in sensors enable continuous measurement of activities of daily living, making them especially well-suited for health research. Researchers have proposed various human activity recognition (HAR) systems aimed at translating measurements from smartphones into various types of physical activity. In this review, we summarized the existing approaches to smartphone-based HAR. For this purpose, we systematically searched Scopus, PubMed, and Web of Science for peer-reviewed articles published up to December 2020 on the use of smartphones for HAR. We extracted information on smartphone body location, sensors, and physical activity types studied and the data transformation techniques and classification schemes used for activity recognition. Consequently, we identified 108 articles and described the various approaches used for data acquisition, data preprocessing, feature extraction, and activity classification, identifying the most common practices, and their alternatives. We conclude that smartphones are well-suited for HAR research in the health sciences. For population-level impact, future studies should focus on improving the quality of collected data, address missing data, incorporate more diverse participants and activities, relax requirements about phone placement, provide more complete documentation on study participants, and share the source code of the implemented methods and algorithms.

Integrating Underwater Data into GIS for Offshore Decommissioning in Bass Strait, Australia

Through asset lifecycle, data is collected for a variety of purposes across multiple disciplines, and exists in various formats and repositories. Decommissioning projects utilize and repurpose a multitude of these datasets; from use in analysis and planning, to facilitating systematic environmental assessments, and meaningful discussion with stakeholders. The key challenge is how do we consolidate historical data, incorporate new data, and make it evergreen to support planning and informed decision making; and how do we coordinate large volumes of previously disparate data in a meaningful way for all users with a simple access model? A team of geographic information system (GIS) practitioners and subject matter contacts in technical and health, safety and environment (HSE) disciplines was convened to collect, sort, and compile known historical offshore data, including, but not limited to; pipeline and structural inspections and environmental studies, all captured via Remote Operated Vehicle (ROV), Side Scan Sonar (SSS), and sampling programs. Data was reformatted to standardize headers and attributes allowing for merging of existing like-data and to support new data integration. To this end, we also worked collaboratively with vendors to optimize data collection and improve alignment with our internal data structures. The Esri GIS technology was utilized for data integration, specifically the web and mobile environments. Through these environments, non-GIS users could easily access data and focused applications, supporting ease of data visualization and allowing for a single view of data spanning decades and covering multiple themes. This enabled an enhanced understanding of the offshore environment, allowing us to identify gaps and focus areas for future data capture, helping to facilitate cross-discipline discussions, and identification of operational synergies; improving access, efficiency, and reducing decommissioning costs. Data integration resulting from this initiative and delivery through a spatially aware GIS environment is providing unprecedented access to a vast scope of cross-disciplinary data previously not possible with more traditional engineering methods and data formats. Data accessibility aids communication, and when combined with early engagement across multi-disciplinary teams, the path to decision making is reduced, synergies gained, and costs are reduced through improved efficiency and optimization.

Graphical representations of patient tolerability data: Recommendations from the National Cancer Institute (NCI) Cancer Moonshot Standardization Working Group.

e18612 Background: Effective communication of treatment tolerability data is essential for clinical decision making and improved patient outcomes, yet standardized approaches to the analysis and visualization of tolerability data in cancer clinical trials are currently limited. To address this need, the Standardization Working Group (SWG) was established within the NCI Cancer Moonshot Tolerability Consortium. This abstract describes the SWG’s initiative to develop a publicly accessible online toolkit with a comprehensive set of guidelines, references, and resources for graphical displays of tolerability data. Methods: A multidisciplinary group of PRO researchers including biostatisticians, clinicians, epidemiologists, and representatives from the NCI and FDA convened monthly to discuss toolkit development and content. Considerations for standardization of graphical displays of tolerability data included (1) types of graphical displays, (2) incorporation of missing data, (3) labeling and color schemes, and (4) software to produce graphical displays. For consistency, considerations of tolerability relied on the Patient-Reported Outcomes version of the CTCAE (PRO-CTCAE), which includes 124 items assessing the frequency, severity, interference, and/or presence of 78 symptomatic adverse events. Graphical displays were generated using simulated PRO-CTCAE data and summarized by composite score (range 0-3).Color schemes that were Section 508 compliant and color blindness accessible were created. Surveys were distributed to 68 consortium members to assess preferences and interpretability of the graphical displays. Results: The SWG created graphical displays for PRO-CTCAE data, including bar charts, butterfly plots, and Sankey diagrams and compiled SAS macros and R functions to do so. Graphical displays made available in the toolkit maximize the use of PRO-CTCAE data, incorporate missingness, support between-arm comparisons, and present data longitudinally over treatment cycles or study timepoints. Survey results for labeling and color schemes were summarized and informed a list of short labels for PRO-CTCAE items (e.g., “radiation burns” for “skin burns from radiation”) and standardized color schemes for use in graphical displays. Survey results were also summarized to provide insight into PRO researchers’ ability to accurately interpret the graphical displays. Conclusions: Standardizinggraphical displays is important for improving the communication and interpretation of tolerability data. The type of graphical display used depends on the purpose of the analysis and should be tailored to the intended audience, including patients. This toolkit will provide a comprehensive resource with best practice recommendations.

Labor Unions and American Poverty

American poverty research largely neglects labor unions. The authors use individual-level panel data, incorporate both household union membership and state-level union density, and analyze both working poverty and working-aged poverty (among households led by 18- to 64-year-olds). They estimate three-way fixed effects (person, year, and state) and fixed-effects individual slopes models on the Panel Study of Income Dynamics (PSID), 1976–2015. They exploit the higher quality income data in the Cross-National Equivalent File—an extension of the PSID—to measure relative (<50% of median in current year) and anchored (<50% of median in 1976) poverty. Both union membership and state union density have statistically and substantively significant negative relationships with relative and anchored working and working-aged poverty. Household union membership and state union density significantly negatively interact, augmenting the poverty-reducing effects of each. Higher state union density spills over to reduce poverty among non-union households, and there is no evidence that higher state union density worsens poverty for non-union households or undermines employment.

Labor Unions and American Poverty

American poverty research largely neglects labor unions. We use individual-level panel data, incorporate both household union membership and state-level union density, and analyze both working and working-aged poverty. We estimate three-way fixed-effects (person, year, and state) and fixed-effects individual slopes models on the Panel Study of Income Dynamics (PSID) 1976-2015. We exploit the Cross-National Equivalent File’s – an extension of the PSID – higher quality income data to measure relative and anchored poverty. Both union membership and state union density have statistically and substantively significant negative relationships with relative and anchored working and working-aged poverty. Household union membership and state union density significantly negatively interact, augmenting the poverty-reducing effects of each. Higher state union density spills over to reduce poverty among non-union households, and there is no evidence that higher state union density worsens poverty for non-union households or undermines employment.

INITIATING OPEN INNOVATION COLLABORATIONS BETWEEN INCUMBENTS AND STARTUPS: HOW CAN DAVID AND GOLIATH GET ALONG?

In recent years, the phenomenon of open innovation has been on the rise in established firms, especially in terms of collaboration with startups. While the success factors of open innovation endeavours have been researched intensively, how collaborations are established is not well understood. Furthermore, there is a lack of research regarding asymmetric partnerships in open innovation, occurring when incumbents and startups collaborate. This study used a qualitative research design to approach the question of how incumbents select startups as partners in open innovation. The data incorporate the perspectives of both incumbents and startups along with the views of external experts. The findings are consolidated into a process model of partner selection for open innovation.

Statistical Models of Holland Pressure Profile Parameter and Radius to Maximum Winds of Hurricanes from Flight-Level Pressure and H*Wind Data

In many hurricane risk models the inclusion of the Holland B parameter plays an important role in the risk prediction methodology. This paper presents an analysis of the relationship between B and a nondimensional intensity parameter. The nondimensional parameter includes the strong negative correlation of B with increasing hurricane size [as defined by the radius to maximum winds (RMW)] and latitude as well as a positive correlation with sea surface temperature. A weak positive correlation between central pressure deficit and B is also included in the single parameter term. Alternate statistical models relating B to RMW and latitude are also developed. Estimates of B are derived using pressure data collected during hurricane reconnaissance flights, coupled with additional information derived from the Hurricane Research Division’s H*Wind snapshots of hurricane wind fields. The reconnaissance data incorporate flights encompassing the time period 1977 through 2001, but the analysis was limited to include only those data collected at the 700-hPa-or-higher level. Statistical models relating RMW to latitude and central pressure derived from the dataset are compared to those derived for U.S. landfalling storms during the period 1900–2005. The authors find that for the Gulf of Mexico, using only the landfall hurricanes, the data suggest that there is no inverse relationship between RMW and the central pressure deficit. The RMW data also demonstrate that Gulf of Mexico hurricanes are, on average, smaller than Atlantic Ocean hurricanes. A qualitative examination of the variation of B, central pressure, and radius to maximum winds as a function of time suggests that along the Gulf of Mexico coastline (excluding southwest Florida), during the final 6–24 h before landfall, the hurricanes weaken as characterized by both an increase in central pressure and the radius to maximum winds and a decrease in B. This weakening characteristic of landfalling storms is not evident for hurricanes making landfall elsewhere along the U.S. coastline.