Wildfires and Monsoons: Cryptic Drivers for Highly Variable Provenance Signals within a Carboniferous Fluvial System

Sediment delivery and supply are explicitly controlled by variations in broad-scale processes such as climate, tectonics and eustasy. These in turn influence fluvial processes and hinterland evolution. A bespoke multi-proxy approach (integrating apatite and zircon U-Pb geochronology, trace elements in apatite, and Pb-in-K-feldspar provenance tools) coupled with outcrop investigation is used to constrain the temporal trends in sediment delivery to channel sandstones of the fluvio-estuarine mid-Viséan Mullaghmore Sandstone Formation, Ireland. Provenance data indicate unique detrital signatures for all sampled horizons, indicating the fluctuating nature of sediment supply to this medium-sized basin. Tectonism and/or abrupt relative sea-level fall likely caused fluvial rejuvenation, resulting in local basement sourcing of the initial fill. Older and more distal sources, such as the Nagssugtoqidian Belt of East Greenland, become more prominent in stratigraphically younger channel sandstones suggesting catchment expansion. Paleoproterozoic to Mesoproterozoic sources are most dominant, yet the detrital grain cargo varies in each channel sandstone. Proximal sources such as the Donegal Batholith and Dalradian Supergroup are variable and appear to switch on and off. These signal shifts are likely the result of channel migration and paleoclimatic fluctuation. A monsoonal climate and large-scale wildfire events (evidenced by fusain) likely contributed to modify plant cover, intensify erosion, and increase run-off and sediment delivery rates from specific areas of the hinterland.

An empirical study on Resource Description Framework reification for trustworthiness in knowledge graphs

Knowledge graph (KG) publishes machine-readable representation of knowledge on the Web. Structured data in the knowledge graph is published using Resource Description Framework (RDF) where knowledge is represented as a triple (subject, predicate, object). Due to the presence of erroneous, outdated or conflicting data in the knowledge graph, the quality of facts cannot be guaranteed. Trustworthiness of facts in knowledge graph can be enhanced by the addition of metadata like the source of information, location and time of the fact occurrence. Since RDF does not support metadata for providing provenance and contextualization, an alternate method, RDF reification is employed by most of the knowledge graphs. RDF reification increases the magnitude of data as several statements are required to represent a single fact. Another limitation for applications that uses provenance data like in the medical domain and in cyber security is that not all facts in these knowledge graphs are annotated with provenance data. In this paper, we have provided an overview of prominent reification approaches together with the analysis of popular, general knowledge graphs Wikidata and YAGO4 with regard to the representation of provenance and context data. Wikidata employs qualifiers to include metadata to facts, while YAGO4 collects metadata from Wikidata qualifiers. However, facts in Wikidata and YAGO4 can be fetched without using reification to cater for applications that do not require metadata. To the best of our knowledge, this is the first paper that investigates the method and the extent of metadata covered by two prominent KGs, Wikidata and YAGO4.

Multimodal Provenance-based Analysis of Collaboration in Business Processes

Modeling business processes as a set of activities to accomplish goals naturally makes them be executed several times. Usually, such executions produce a large portion of provenance data in different formats such as text, audio, and video. Such a multiple-type nature gives origin to multimodal provenance data. Analyzing multimodal provenance data in an integrated form may be complex and error-prone when manually performed as it requires extracting information from free-text, audio, and video files. However, such an analysis may generate valuable insights into the business process. The present article presents MINERVA (Multimodal busINEss pRoVenance Analysis). This approach focuses on identifying improvements that can be implemented in business processes, as well as in collaboration analysis using multimodal provenance data. MINERVA was evaluated through a feasibility study that used data from a consulting company.

Online Deep Learning Hyperparameter Tuning based on Provenance Analysis

Training Deep Learning (DL) models require adjusting a series of hyperparameters. Although there are several tools to automatically choose the best hyperparameter configuration, the user is still the main actor to take the final decision. To decide whether the training should continue or try different configurations, the user needs to analyze online the hyperparameters most adequate to the training dataset, observing metrics such as accuracy and loss values. Provenance naturally represents data derivation relationships (i.e., transformations, parameter values, etc.), which provide important support in this data analysis. Most of the existing provenance solutions define their own and proprietary data representations to support DL users in choosing the best hyperparameter configuration, which makes data analysis and interoperability difficult. We present Keras-Prov and its extension, named Keras-Prov++, which provides an analytical dashboard to support online hyperparameter fine-tuning. Different from the current mainstream solutions, Keras-Prov automatically captures the provenance data of DL applications using the W3C PROV recommendation, allowing for hyperparameter online analysis to help the user deciding on changing hyperparameters’ values after observing the performance of the models on a validation set. We provide an experimental evaluation of Keras-Prov++ using AlexNet and a real case study, named DenseED, that acts as a surrogate model for solving equations. During the online analysis, the users identify scenarios that suggest reducing the number of epochs to avoid unnecessary executions and fine-tuning the learning rate to improve the model accuracy.

Basin evolution in response to flat subduction in the Altiplano

This contribution assesses models for basin formation in the Altiplano. New magnetostratigraphy, palynology, and 40Ar/39Ar and U-Pb geochronology from the central Corque Syncline demonstrate that the 7.4 km-thick section was deposited between 36.7 and 18.7 Ma. The base of the section post-dates exhumation in both the Western and Eastern cordilleras, precluding deposition in a classic retroarc foreland basin setting. Rotated paleomagnetic vectors indicate counterclockwise rotation of 0.8°/Myr since the early Oligocene. Detrital zircon provenance data confirm previous interpretations of Eocene–early Oligocene derivation from the Western Cordillera and a subsequent switch to an Eastern Cordilleran source. Flexural modeling indicates that loads consistent with paleoelevation estimates cannot account for all subsidence. Rather, the timing and magnitude of subsidence is consistent with Eocene emplacement and Oligocene–early Miocene resteepening of a flat slab. Integration of the magmatic, basin, and deformation history provides a coherent model of the effects of flat subduction on the overriding plate. In this model flat subduction controlled basin formation in the upper plate, with subsidence enhanced in front of the zone of flat subduction but reduced over the crest of the flat slab. We conclude that the Altiplano was conditioned for plateau formation by Eocene–Oligocene flat subduction.Thematic collection: This article is part of the Fold-and-thrust belts collection available at: https://www.lyellcollection.org/cc/fold-and-thrust-beltsSupplementary material:https://doi.org/10.6084/m9.figshare.c.5664345

Response of Northern Populations of Black Spruce and Jack Pine to Southward Seed Transfers: Implications for Climate Change

A variety of responses to climate change have been reported for northern tree populations, primarily from tree-ring and satellite-based studies. Here we employ provenance data to examine growth and survival responses of northern populations (defined here as those occurring north of 52° N) of black spruce (Picea mariana) and jack pine (Pinus banksiana) to southward seed transfers. This space for time substitution affords insights into potential climate change responses by these important northern tree species. Based on previous work, we anticipated relatively flat response curves that peak at much warmer temperatures than those found at seed source origin. These expectations were generally met for growth-related responses, with peak growth associated with seed transfers to environments with mean annual temperatures 2.2 and 3.6 °C warmer than seed source origin for black spruce and jack pine, respectively. These findings imply that northern tree populations harbor a significant amount of resilience to climate warming. However, survival responses told a different story, with both species exhibiting reduced survival rates when moved to warmer and drier environments. Together with the growth-based results, these findings suggest that the warmer and drier conditions expected across much of northern Canada under climate change may reduce survival, but surviving trees may grow at a faster rate up until a certain magnitude of climate warming has been reached. We note that all relationships had high levels of unexplained variation, underlining the many factors that may influence provenance study outcomes and the challenges in predicting tree responses to climate change. Despite certain limitations, we feel that the provenance data employed here provide valuable insights into potential climate change outcomes for northern tree populations.

User Steering Support in Large-scale Workflows

Large-scale workflows that execute on High-Performance Computing machines need to be dynamically steered by users. This means that users analyze big data files, assess key performance indicators, fine-tune parameters, and evaluate the tuning impacts while the workflows generate multiple files, which is challenging. If one does not keep track of such interactions (called user steering actions), it may be impossible to understand the consequences of steering actions and to reproduce the results. This thesis proposes a generic approach to enable tracking user steering actions by characterizing, capturing, relating, and analyzing them by leveraging provenance data management concepts. Experiments with real users show that the approach enabled the understanding of the impact of steering actions while incurring negligible overhead.

A Proposal for Sharing Software Process Provenance Data in Heterogeneous Environments

Software development practices have evolved, and new approaches have emerged, like Global Software Development (GSD). In addition, software development companies started to adopt data-driven practices in parts of their business. However, using and sharing software process data in a distributed and heterogeneous environment, like the GSD context, could be a challenging topic for many software engineers. In this paper, we present a proposal for sharing software process provenance data using a model that extends PROV, the PROV- SwProcess model. An example of applying this model using a process from the industry that deals with error handling and the implementation of new features in an Enterprise Resource Planning system is presented and explains how the model allows sharing software process provenance data, in addition to providing inferences and insights about these data.

Resource Description Framework reification for trustworthiness in knowledge graphs

Knowledge graph (KG) publishes machine-readable representation of knowledge on the Web. Structured data in the knowledge graph is published using Resource Description Framework (RDF) where knowledge is represented as a triple (subject, predicate, object). Due to the presence of erroneous, outdated or conflicting data in the knowledge graph, the quality of facts cannot be guaranteed. Therefore, the provenance of knowledge can assist in building up the trust of these knowledge graphs. In this paper, we have provided an analysis of popular, general knowledge graphs Wikidata and YAGO4 with regard to the representation of provenance and context data. Since RDF does not support metadata for providing provenance and contextualization, an alternate method, RDF reification is employed by most of the knowledge graphs. Trustworthiness of facts in knowledge graph can be enhanced by the addition of metadata like the source of information, location and time of the fact occurrence. Wikidata employs qualifiers to include metadata to facts, while YAGO4 collects metadata from Wikidata qualifiers. RDF reification increases the magnitude of data as several statements are required to represent a single fact. However, facts in Wikidata and YAGO4 can be fetched without using reification. Another limitation for applications that uses provenance data is that not all facts in these knowledge graphs are annotated with provenance data. Structured data in the knowledge graph is noisy. Therefore, the reliability of data in knowledge graphs can be increased by provenance data. To the best of our knowledge, this is the first paper that investigates the method and the extent of the addition of metadata of two prominent KGs, Wikidata and YAGO4.