Modeling vague spatial data warehouses using the VSCube conceptual model

Spatial Data Warehouses (SDW) and Spatial OLAP (SOLAP) systems are well-known Business Intelligence technologies that aim to support multidimensional and online analysis of huge volumes of datasets with spatial reference. Spatial vagueness is one of the most neglected imperfections of spatial data. Although several works propose new ad-hoc models for handling spatial vagueness in information systems, the implementation of those models in Spatial DBMS and SDW is still in an embryonic state. Thus, in this paper, we present a new design method for SOLAP datacubes that allows handling vague spatial data analysis issues. This method relies on a risk management method applied to the potential risks of data misinterpretation and decision-makers’ tolerance levels to those risks. We also present a system implementing our method.

With a huge amount of data stored in spatial databases and the introduction of spatial components to many relational or object-relational databases, it is important to study the methods for spatial data warehousing and OLAP of spatial data. In this paper, we study methods for spatial OLAP, by integrating nonspatial OLAP methods with spatial database implementation techniques. A spatial data warehouse model, which consists of both spatial and nonspatial dimensions and measures, is proposed. Methods for the computation of spatial data cubes and analytical processing on such spatial data cubes are studied, with several strategies being proposed, including approximation and selective materialization of the spatial objects resulting from spatial OLAP operations. The focus of our study is on a method for spatial cube construction, called object-based selective materialization, which is different from cuboid-based selective materialization (proposed in previous studies of nonspatial data cube construction). Rather than using a cuboid as an atomic structure during the selective materialization, we explore granularity on a much finer level: that of a single cell of a cuboid. Several algorithms are proposed for object-based selective materialization of spatial data cubes, and a performance study has demonstrated the effectiveness of these techniques.

professional database especially the data center management model has achieved the integration, share of data and the separation between data and application in oil exploration and development, but the issues for data analysis and data mining, especially the professional data mining, are not well solved. In this paper, the necessity of spatial data warehouse used in oil and gas exploration and development is analyzed, data management framework based on the spatial data warehouse for oil and gas exploration and development is proposed, the construction steps of spatial data warehouse are provided, the differences between spatial data warehouse and other data management model are given, the key issues the construction of spatial data warehouse faces are analyzed.

Spatial big data architecture: From Data Warehouses and Data Lakes to the LakeHouse

With the increasing development of the application of location services, massive check-in data is produced by social media applications on the mobile appliances, which includes characteristics of spatio-temporal information, user-emotion information, and etc. Traditional analysis techniques cannot handle check-in data well because of the complexity of spatio-temporal information. Spatial data warehouse provided a good architecture for spatial data’s storage and analysis. In this research, we designed a spatial data warehouse to store and manage the check-in data, used OLAP analysis technology to analyze it, and found many interesting results. It showed spatial data warehouse and OLAP provided a good frame to analyze check-in data.

A spatial data warehouse (SDW) is constructed to support the spatial data analysis for decision support purposes. Selectively materializing spatial views to rewrite input queries and thus reduce query response time is a challenging issue for spatial query evaluations. In this paper, we first investigate the issue of using spatial metadata to construct a spatial view dependency framework, which implies an order to materialize views. We then propose a cost model to evaluate the cost for processing spatial queries, which measures the online computation vs. space cost for spatial queries. A greedy algorithm is introduced to materialize a set of views based on the view dependence framework with associated cost value at each dependence level, which shows the local cost optimality of designing an SDW.

Conjugation of δ‐endotoxin from Bacillus thuringiensis with abamectin, a toxin of Streptomyces avermitilis, was carried out to form a new type of biocide, GCSC‐BtA based on “Germany‐China Scientific Cooperation” research, for the control of agricultural insect pests. The strategy for biochemical linkage was designed by conjugating an amino group in B.t. protoxin with a carboxyl group in carboxylated abamectin under the treatment of conjugator EDC [1‐Ethyl‐3‐(3‐dimethylaminopropyl carbodiimide Hydrochloride)]. The formation of B.t. protoxin was processed by solubilizing B.t. crystal in 25 mM dithiothreitol (DTT) at 37 °C for 2 h. The carboxylated abamectin was formed by carboxylating the NaH‐activated abamectin with 10 mg/ml butyric anhydride at 111 °C in a water‐circumfluent condensation device for 2 h. The conjugating reaction, consisting of 5 mg/ml B.t. protoxin, 10 mg/ml carboxylated abamectin and 19.17 mg/ml EDC, was successfully conducted at room temperature for 24 h. Significant differences were found between pure abamectin, carboxylated abamectin and the conjugated BtA by means of UV‐photo absorptions recorded at wavelengths 354, 438, 518, 600 nm (P < 0.01). LT50 of the conjugated GCSC‐BtA to the 3rd instar larvae of Plutella xylostella (L.) (Lep., Plutellidae) was 35.27 μg a.i./ml, about 62 % and 76 % of that caused by the B.t. protoxin and the caxboxylated abamectin, respectively. The conjugated GCSC‐BtA caused 87.14 % mortalities in larvae of P. xylostella, 93.75 % in adult Myzus persicae (Sulzer) (Hom., Aphididae) and 89.33 % in adult Phyllotreta vittata Fabricius (Col., Chrysomelidae) as compared to 48.33 % by the B.t. crystal only in P. xylostella. The symptoms caused by conjugated GCSC‐BtA in the 3rd instar of P. xylostella were black color in the head part and white‐yellow in the abdomen of dead larvae, which differed from the black color or the white‐yellow all along the body caused by either the B.t. crystal or the abamectin, respectively. It was concluded that the conjugated GCSC‐BtA biocide had a broader host spectrum and a faster killing speed than either the B.t. crystal or abamectin alone for the control of agricultural pests.

The design of Spatial OLAP (SOLAP) applications consists of (i) Spatial Data Warehouse (SDW) model design and (ii) SOLAP visualization definition because a specific set of understandable and readable cartographic visualizations corresponds to a particular type of SOLAP query. Unfortunately few works investigate geovisualization issues in SOLAP systems and propose new methodologies to visualize spatio-temporal data, and no works investigate tools for readable SOLAP cartographic displays. Moreover, some works propose ad-hoc methodologies for DWs and SDWs exclusively based on data and user analysis requirements. Therefore, we present in this paper (i) a new geovisualization methodology for SOLAP queries that yields readable maps and (ii) a new prototyping design methodology for SOLAP applications that accounts for geovisualization requirements.

In this paper we present a Spatial Data Warehouse system that we use for aggregation and analysis of huge amounts of spatial data. The data is generated by utilities meters communicating via radio. In order to provide sufficient efficiency for our system we propose data and workload distribution as well as advanced indexing techniques. The system is based on a cascaded star model, which is a spatial development of a standard star schema and contains interconnected and often nested star schemas. The cascaded star allows efficient storage and analysis of spatial data, whose range extends from meter measurement values to weather information. The indexing tree structure and operation is tightly integrated with the spatial character of the data. Thanks to an available memory evaluating mechanism the system is very flexible in the field of aggregates accuracy. We also implemented indexing structure updating mechanism. The system is written in Java; for the data base we use Oracle 9i. Basing on the wide variety of tests results, we prove that a distributed system significantly surpasses the centralized version in terms of efficiency. We also show that a selective materialization of indexing structure fragments strongly increases system efficiency.

Spatial data generated by an Internet of Things (IoT) network is important to assist the spatial analytics process in issues related to smart cities. In these cities, IoT devices generate spatial data constantly. Thus, data can get increasingly voluminous very fast. In this paper, we investigate the challenge of managing these data through the use of a spatial data warehouse designed over a parallel and distributed data processing framework extended with a spatial analytics system. We propose an architecture aimed to assist a smart cities manager in the decision-making process. This architecture integrates a cloud layer where these technologies are located with a fog computing layer for extracting, transforming and loading the data into the spatial data warehouse. Furthermore, we introduce a set of guidelines to aid smart cities managers to implement the proposed architecture. These guidelines describe and discuss important issues that should be faced by the managers. We validate our architecture with a case study that uses real data collected by IoT devices in a smart city. This case study encompasses the execution of three different categories of spatial queries, demonstrating the architecture's efficacy and effectiveness to support spatial analytics in the context of smart cities.

In order to study nowcasting operations using spatial data mining, a spatial data warehouse must be built via ETL process to store radar spatial data reflects strong convection weather. Also, the quality control of radar spatial data in the ETL process, which directed at weather station observed data and radar spatial data. based on dynamic incremental rule engine(DIRE) and ISTDW method. The experimental result demonstrates that DIRE can effectively uncover the abnormal value in radar data and that ISTDW can estimate the absent values in radar data via interpolation with the tolerance of errors.

Common birds combine pest control and seed dispersal in apple orchards through a hybrid interaction network

The mechanism of damage to the posterior silk gland by trace amounts of acetamiprid in the silkworm, Bombyx mori

Bacillus thuringiensis is gaining worldwide importance in the control of pests in agriculture and public health. Characterization of new isolated strains with original, higher and broader spectrum of activity is an ever developing field. Parasporal inclusions from BLB427, a new B. thuringiensis strain isolated from Tunisian soil, contained 2 major Cry proteins having molecular weights of about 130-135 kDa and 65-70 kDa corresponding to cry1-type and cry2-type genes, respectively. These toxins exhibited high toxicities to Ephestia kuehniella and Spodoptera littoralis larvae, with LC50 values of about 81.91 µg/cm2 (+/- 15.30) and 79.70 µg/cm2 (+/- 36.73), respectively. BLB427 supernatant showed a promising activity against Lepidopteran pests due to the presence of vip3-type gene in this strain. The high toxicity of BLB427 supernatant compared to that of BUPM95, used as control, was due in part to the high copy number of vip3 gene of BLB427 compared to that of vip3Aa16 of BUPM95, as demonstrated by Real-time PCR. This gene was cloned, sequenced and the comparison with the most known vip3 genes in databases, demonstrated that vip3(427) gene and the corresponding protein showed differences that can influence the efficiency of the Vip3(427) toxin against Lepidoptera. After overexpression of Vip3(427) protein in Esherichia coli and its purification using His-Trap column, the toxin showed a promising toxicity against the lepidopteran pest S. littoralis with extremely damages in the larvae midgut traduced by the vacuolization of the apical cells, the damage of microvilli and the disruption of epithelial cells. The results described in the present study proved that B. thuringiensis BLB427 strain could be of a great interest for lepidopteran biocontrol by using its d-endotoxins and/or Vip3 toxins in bioinsecticides formulations.

Modelling the spatial distribution of evapotranspiration on different scales using remote sensing data