Laboratory investigation on stone matrix asphalt using banana fiber

The technology and usage of the asphalt materials and mixtures is first discovered and mostly used in European countries and North America. The SMA (stone matrix asphalt) mixture is a gap-graded mix which is characterized by high coarse aggregates, high asphalt contents and fiber additives as stabilizers. In this present research, an attempt has been made to study the engineering properties of mixtures of stone matrix asphalt with and without fiber. Here fiber used is a non-conventional natural fiber, namely banana fiber. This research was done to check the suitability of banana fibre as stabilising agent in the mixture by laboratory tests in which a flow parameter and stability were analyzed, as well as the mechanical properties of the mixture. Here for the stone matrix asphalt mix the aggregate gradation is taken based on the MoRTH specification and the binder content is 4%, 4.5%. 5%, 5.5%, 6%, 6.5%, 7% by weight of aggregate and fibre used is 0.3% by weight of aggregate. Here cement is used as filler and binder used is 60/70 grade bitumen

Mobility Data Science (Dagstuhl Seminar 22021)

This report documents the program and the outcomes of Dagstuhl Seminar 22021 "Mobility Data Science". This seminar was held January 9-14, 2022, including 47 participants from industry and academia. The goal of this Dagstuhl Seminar was to create a new research community of mobility data science in which the whole is greater than the sum of its parts by bringing together established leaders as well as promising young researchers from all fields related to mobility data science. Specifically, this report summarizes the main results of the seminar by (1) defining Mobility Data Science as a research domain, (2) by sketching its agenda in the coming years, and by (3) building a mobility data science community. (1) Mobility data science is defined as spatiotemporal data that additionally captures the behavior of moving entities (human, vehicle, animal, etc.). To understand, explain, and predict behavior, we note that a strong collaboration with research in behavioral and social sciences is needed. (2) Future research directions for mobility data science described in this report include a) mobility data acquisition and privacy, b) mobility data management and analysis, and c) applications of mobility data science. (3) We identify opportunities towards building a mobility data science community, towards collaborations between academic and industry, and towards a mobility data science curriculum

IN-DATABASE RASTER ANALYTICS: MAP ALGEBRA AND PARALLEL PROCESSING IN ORACLE SPATIAL GEORASTER

Over the past decade several products have been using enterprise database technology to store and manage geospatial imagery and raster data inside RDBMS, which in turn provides the best manageability and security. With the data volume growing exponentially, real-time or near real-time processing and analysis of such big data becomes more challenging. Oracle Spatial GeoRaster, different from most other products, takes the enterprise database-centric approach for both data management and data processing. This paper describes one of the central components of this database-centric approach: the processing engine built completely inside the database. Part of this processing engine is raster algebra, which we call the In-database Raster Analytics. This paper discusses the three key characteristics of this in-database analytics engine and the benefits. First, it moves the data processing closer to the data instead of moving the data to the processing, which helps achieve greater performance by overcoming the bottleneck of computer networks. Second, we designed and implemented a new raster algebra expression language. This language is based on PL/SQL and is currently focused on the "local" function type of map algebra. This language includes general arithmetic, logical and relational operators and any combination of them, which dramatically improves the analytical capability of the GeoRaster database. The third feature is the implementation of parallel processing of such operations to further improve performance. This paper also presents some sample use cases. The testing results demonstrate that this in-database approach for raster analytics can effectively help solve the biggest performance challenges we are facing today with big raster and image data

Spatial databases: Accomplishments and research needs

Spatial databases have been an active area of research for over two decades, addressing the growing data management and analysis needs of spatial applications such as Geographic Information Systems. This research has produced a taxonomy of models for space, spatial data types and operators, spatial query languages and processing strategies, as well as spatial indexes and clustering techniques. However, more research is needed to improve support for network and eld data, as well as query processing (e.g. cost models, bulk load). Another important need is to apply the spatial data management accomplishments to newer applications such as data warehouses and multimedia information systems. The objective of this paper is to identify recent accomplishments and the research needs in the near term

Spatial Databases: Accomplishments and Research Needs

Spatial databases have been an active area of research for over two decades, addressing the growing data management and analysis needs of spatial applications such as Geographic Information Systems. This research has produced a taxonomy of models for space, spatial data types and operators, spatial query languages and processing strategies, as well as spatial indexes and clustering techniques. However, more research is needed to improve support for network and field data, as well as query processing (e.g. cost models, bulk load). Another important need is to apply the spatial data management accomplishments to newer applications such as data warehouses and multimedia information systems. The objective of this paper is to identify recent accomplishments and the research needs in the near term. Keywords: Spatial Databases, Multi-Dimensional, Object-Relational, Databases, Geographic Information Systems 1 Introduction 1.1 Spatial Databases A spatial database [11, 15, 35] manag..

Point cloud data management (extended abstract)

Point cloud data are important sources for 3D geo-information. The point cloud data sets are growing in popularity and in size. Modern Big Data acquisition and processing technologies, such as laser scanning from airborne, mobile, or static platforms, dense image matching from photos, multi-beam echo-sounding, or from autotracking seismic data, have the potential to generate point clouds with millions or billions (or even trillions) of 3D points (with in many cases one or more attributes attached). This is especially true with the available and expected repeated scans of same area (the temporal dimension). These point clouds are too massive to be handled efficiently by common geo-ICT infrastructures. At the database level, initial implementations are available in both commercial and open source database products, illustrating the user need for point cloud support; e.g. Oracle spatial’s SDO_PC data type and PostgreSQL/PostGIS PCPATCH data type. This new data type should be available in addition to the existing vector and raster data types. Further, a new and specific web-services protocol for point cloud data is investigated, supporting progressive transfer based on multi-resolution. The eScience project investigates solutions in order to better exploit the rich potential of point cloud data. The project partners are: Rijkswaterstaat (RWS), Fugro, Oracle, Netherlands eScience Centre and TU Delft. An inventory of the user requirements has been made using structured interviews with users from different background: government, industry and academia. Based on these requirements a benchmark has been developed to compare various point cloud data management solutions w.r.t. functionality and performance. The main test data set is the second national height map of the Netherlands, AHN2, with 6 to 10 samples for every square meter of the country, resulting in more than 600 billion points with 3 cm accuracy.OTBArchitecture and The Built Environmen

Mobility Data Science: Perspectives and Challenges

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the domain of mobility data science. Towards a unified approach to mobility data science, we present a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art, and describe open challenges for the research community in the coming years