dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, the data warehousing process is actually the most laborious task in designing a DW, being complicated, expensive, and hard to fulfill. Moreover, according to  [14, 15] , the excellent design and maintenance of the ETL/ELT process are critical factors for the success of a DW project. For this reason, to guarantee the good and successful physical implementation of a DW project, the focus should be initially placed on the modeling of this process. Despite the multitude of approaches proposed in the literature in this context, most of them are designed primarily to meet specific needs. Therefore, to date, there exists no standard model to represent and design such a process. In this paper, a large literature study was carried out, which allowed us to distinguish many works dealing with the conceptual modeling of ETL/ELT processes.
To better under-stand these works, we propose a new categorization for the studied approaches dealing with data warehousing modeling processes. In fact, ETL process modeling has been gaining importance over the years, allegedly until 2018. Indeed, some authors have distinguished only two types of modeling formalisms in these works  [16] : the UML language and the conceptual constructs. Meanwhile,  [17]  distinguished three types of formalisms: a specific notation, ontology, and the BPMN. Furthermore,  [18]  distinguished four types of models: graph, UML, ontology, and BPMN. With the evolution of Big Data, recent research works have tended to focus more on the deployment of Big Data technologies in the Hadoop ecosystem (e. g. , Spark, Hive, Pig, Storm), among others, without focus on the conceptual modeling of the process.
For this reason, besides classical approaches, we focus also on the new methods proposed for the Big Data warehousing processes in this article. To be more generic and to cover as much of the proposed research in the literature as possible, we categorize these research works into six major classes, according to the modeling formalism on which they are based: 1. ETL process modeling based on UML; 2. ETL process modeling based on ontology; 3. ETL process modeling based on MDA; 4. ETL process modeling based on graphical flow which includes BPMN, CPN, YAWL, and the data visualization flow; 5. ETL process modeling based on ad hoc formalisms, which include conceptual constructs, CommonCube, and EMD; 6. ELT process modeling approaches for Big Data. We noticed that in the literature, other literature reviews have been presented, such as those of  [8, [19] [20] [21] ; however, most of these studies deal with a limited number of approaches, including only some formalisms.
In addition, their comparison was limited to some criteria and did not include Big Data characteristics. To our knowledge, this is the first study to investigate the problem of modeling data warehousing processes both in the classical and Big Data contexts. Our synthetic study aims to offer a global vision of the field of data warehousing system design, ranging from the emergence of ETL processes to the era of Big Data, in order to identify the main problems and current challenges. Thus, the main contributions of this paper are as follows: • We perform an exhaustive study through a systematic literature review in the data warehousing modeling field; The remainder of this paper is organized as follows: Section 2 presents the comparison criteria upon which we base our systematic literature review.
Proposed Classification of ETL/ELT Process Modeling Approaches Although there is no standard model for ETL modeling at present, for several years great effort has been devoted to the study of data warehousing process design. In this section, we propose a new categorization of the studied research works. As follows from Figure  1 , we categorize these chronologically sorted research works into six main classes, according to the modeling formalism on which they are based: (i) Model based on UML  [22] ; (ii) model based on ontology  [23] ; (iii) model based on MDA  [24]  and model-driven development (MDD)  [25] ; (iv) model based on graphical flow formalism, including BPMN  [26] , the CPN modeling language  [27] , YAWL (Yet Another Workflow Language)  [28] , and data flow visualization  [29] ; (v) model based on ad hoc formalisms, including conceptual constructs  [30] , CommonCube  [31] , and EMD  [32] ; and, finally, (vi) contributions dealing with Big Data.
Summary of ETL Process Modeling Approaches Based on UML In  [6] , Trujillo and S. Luján-Mora presented an approach for the conceptual modeling of the ETL process based on UML class diagrams. They modeled the operational data source (ODS), the DW conceptual scheme (DWCS), and the DW physical storage scheme (DWSS). To ensure mapping between the different schemas, they defined two different mapping schemas: (i) an ETL process, defining the mapping between the ODS and the DWCS, and (ii) an exportation process, defining the mapping between the DWCS and the DWSS  [6]. The authors used different extensions of UML, including a UML profile for data modeling  [36]  and Rationale's UML profile for database design  [37]. In addition, they defined a palette of icons to present the most common ETL tasks, called "mechanisms" (aggregation, conversion, filter, incorrect, join, loader, log, merge, substitute, and wrapper).
L. Muñoz et al. in  [33] , used UML activity diagrams to model the sequence of activities of ETL processes. Through the proposed modeling elements, they aimed to represent the dynamic aspects and behavior of an ETL process, arrange the control flow, and incorporate temporality restrictions (known as time constraints), corresponding to the time that a process takes to be executed. This proposal is based on the work previously presented in  [6] , in which each activity was presented through a stereotyped class. The authors in  [33]  proposed a modeling framework composed of two levels. The first level is a high level of abstraction of the ETL process elements (data sources, DW), while the second level is the lower level of abstraction, which presents the sequence flow of an ETL process using the UML activity diagrams meta classes.
As for the transformation phase, they proposed the "log file cleaning" and "session identification" activity diagrams, which identify web surfer sessions and subdivide log files according to different user sessions. Finally, the "business-web mapping" activity maintains the correspondence between different data sources in order to keep a homogeneous file through use of structure unification. The authors proposed a palette "ETL-Web" and an ETL-WEB library as two extensions of the ETL tool Talend Open Studio (TOS), in order to validate the modeling approach. At the physical layer, a Java code was developed and used by TOS to generate a JavaJet executable component. Although three different levels of modeling-conceptual, logical, and physical-were presented in these works, neither the schema of the data sources nor that of the DW was presented.
The proposal of  [35]  used a systems modeling language (SysML) requirement diagram and an activity diagram for the expression of ETL processes. SysML is a graphical modeling language derived from UML, with some additional facilities. According to  [35] , the requirement diagram was used to support text-based requirements, their relationship, and test cases using graphical constructs. In contrast, the activity diagram is deployed to explore system behavior by showing the flow of control and data within activities. In addition, they used MagicDraw to present the SysML requirements diagram of the ETL scenario and the SysML activity diagram to model the ETL conceptually. The XMI (XML Metadata Interchange) executable code corresponding to the ETL model is then generated. In a follow-up work, based on the model-based system engineering (MBSE)-oriented approach, [40] tried to justify the system validation by applying a simulation process.
Finally, the method proposed in  [23]  was validated through experiments using the Lehigh University Benchmark (LUBM) ontology  [57]  and Oracle semantic databases (SDBs). In contrast, the work of  [56]  presented the validation of their model by using a practical case study from the medical domain. Another extension of this work was detailed in  [56] , by proposing and implementing a new ETL approach analyzing class dependencies for efficiently managing an ontology at two layers: Canonical and non-canonical. Therefore, we underline the aspects of reusability and continuity in these research works. Despite the richness of these works, the formally defined mapping schemes were not presented graphically, and no details on the ETL process requirements were given. In  [45] , SETL, a Python-based programmable semantic ETL framework for semantic data processing and integration by linking semantic web and DW technologies, was proposed.
SETL offers the different modules needed for both dimensional and semantic DW constructs and tasks. It supports RDF data sources, semantic integration, and the creation of a semantic DW using a target ontology. Ontology data and their instances are semantically connected with other internal and/or external data. This allows the framework to be opened up to other new entities from other datasets. Moreover, they conducted an experimental evaluation to demonstrate its performance, based on the execution time, knowledge base quality, and programmer productivity. In  [58] , DebNath et al. presented more details on the SETL framework; however, the conceptual modeling of the model was not specified, which complicates prior understanding of the sequence of actions in SETL. In software design, according to  [48] , "Patterns can be characterized using a set of pre-established tasks grouped based on a specific configuration related to the context in which they are used.
2. 1). To define the meta-models of the developed PIM and PSM, they used the UML object diagram. Finally, in order to validate their approach, they applied QVT transformations for automatically generating code to execute the ETL process on the specific platform Oracle Warehouse Builder (OWB) as a PSM from a UML activity diagram as PIM. Moreover, in these works, the authors distinguished four types of transformations: transforming activities, transforming actions, transforming data stores, and transforming parameters. The efforts proposed by Maté et Trujillo in 2012  [65]  and in 2014  [66]  are considered a continuation of both the proposal of  [64] , where the requirements are specified in a CIM employing a UML profile based on the i* framework, and the proposal mentioned in  [24] , involving a hybrid DW development approach in the context of the MDA framework.
Summary of ETL Process Modeling Approaches Based on YAWL In  [28] , Belo et al. proposed a pattern-oriented meta-model for supporting the conceptual modeling of the data quality validation (DQV) task, which is one of the most common ETL tasks. After that, they instantiated this DQV ETL pattern using the YAWL workflow language. YAWL provides a powerful engine that allows for the specification of data related to the execution of a system model. Finally, they schematized a YAWL-aware ETL pattern supporting three pairs of procedures: (i) decomposition and standardization; (ii) validation and correction; and, finally, (iii) duplicate elimination. However, this effort lacked a link to the logic model.
The KANTARA architecture is composed of three levels. The "model edition" level is for the conceptual modeling of the ETL process, which is based on six components: data profiling, design environment, checks and control, change manager, manager and scheduler, and testing and quality. These different components are detailed in  [85, 86]. The outputs of this level are conceptual models of ETL processes, which are independent of any platform (i. e. , PIM models). The "transformations" level applies transformations to previous PIM models to generate code to transfer to the next level. Finally, the "execution and integration" level executes the code received from the previous level. Moreover, in  [86] , the authors presented a meta-model of the design environment module, which presents the conceptual model of the ETL process.
ELT Process Modeling Approaches for Big Data The ETL process modeling approaches proposed within the framework of traditional operational information systems cannot cope with the emergence of the new wave of "Big Data". Therefore, new methods must be deployed to address the massive volumes of source data, semi-structured and unstructured data, complex data processing, rapid decision making, and quality of data delivered. Several studies sought to address Big Data; however, their focus was mostly on the deployment of technologies without taking into account the importance of conceptual modeling, which is often overlooked. In this section, we present some recent contributions. These methods are based on the deployment of parallel and distributed processing (HDFS for storage and MapReduce for processing), on the use of NoSQL database management systems, or on others technologies dedicated to Big Data.
The authors separated the modeling aspects of the ETL process as follows: 1. ETL4Social-processes, to manage the control flows of ETL operations; 2. ETL4Social-operations, to manage data flows within an operation; and 3. ETL4Social-data, providing models for social data sources, temporary data, external data, and social DW data. In  [93] , their work focused on detecting topics of interest. In fact, they detailed the architecture of an "ETL4SocialTopic" prototype, and implemented their workflows on Talend Open Studio. This implementation was tested on two real Twitter datasets. Furthermore, in the evaluation of the ETL4SocialTopic results, they were particularly interested in inconsistency. Therefore, they defined topic schema consistency metrics, then they calculated them. In these works, although the authors were interested in modeling the data and control flows using the BPMN language, they did not deal with the temporal evolution aspect of the data collected, which proves the lack of support regarding the frequent changes in social data.
In  [92] , Moalla et al. presented a new approach for DW modeling from social media. This approach allows the analysis of user opinions based on reviews posted on social media, such as Facebook, Twitter, and YouTube. Figure  4  details the various components of the approach. Example of a global approach for a data warehouse schema from social media for opinion analysis  [92]. Researchers rely primarily on data warehousing, in the context of Big Data on not-only-SQL (NoSQL) databases. These databases are also known as schema-less or schema-free, as they allow users to store documents with different structures in the same collection  [95]. Very few studies have addressed ETL process design in the context of NoSQL stores  [96] [97] [98] [99]. In  [99] , Souibgui et al. introduced an approach to extract, transform, and load NoSQL data sources on demand.
This proposed approach considers both data sources of schemaless nature and the analytical needs of users. They described the approach's general architecture which, in the first stage, extracts the global schema of each collection. They ensured a mapping between the global schemas of these collections and their multi-dimensional attributes at the second stage. Afterwards, they performed ETL operations to load data into the DW. Although this approach considers the requirements of the decision-maker, neither an implementation nor a case study was presented to demonstrate its effectiveness. In  [100] , Salinas et al. proposed a multi-layer staggered architecture model for Big Data (MSAM-BD). The proposed model consists of three layers: the data upload layer, in which the structured data are preprocessed and stored in relational databases, while the unstructured data are stored into distributed NoSQL databases; the data processing and storage layer, in which structured data are aggregated, while unstructured ones undergo a categorization and filtering process; and, finally, the data analysis layer, in which the analysis is carried out, according to the longevity of the data.
Lambda+ has two main functionalities: (1) storing data, allowing for flexible and exploratory data analyses; and (2) computing predefined queries on data streams in real time, in order to gain insights regarding well-known and identified needs. This proposed architecture includes five main components: (1) a data traffic controller, which organizes the data sources into streams and realizes some necessary light transformations; (2) master datasets, in which the raw data are stored for reprocessing by leveraging the fault-tolerance property; (3) a streaming ETL component, in which the data are transformed and stored in the storage component; (4) a real-time insights component, which computes predetermined queries in real time; and, finally, (5) a storage component, in which the data are stored and issued for exploratory analysis.
Comparison of ELT Process Modeling Approaches for Big Data This section compares the studied approaches for modeling data warehousing in the context of Big Data, according to the common criteria as mentioned above (Section 2). Moreover, we specified the following additional criteria: Big Data (volume, velocity, variability, veracity), database (relational, NoSQL), and processing (batch, stream), in order to address the Big Data requirements. In the era of Big Data, DW designers face new challenges, including the collection of a variety of data, storing huge volumes of data, processing data arriving in a stream, and responding to real-time needs. Although different approaches were proposed to tackle these challenges in the considered works, most of them did not take into consideration conceptual modeling of the data, which is crucial for the user to be able to specify the data to be collected from diverse sources and in massive volumes.
Forth, they provide an explicit and formal representation, with well-defined semantics that allow for automated reasoning on metadata, including inference rules to derive new information from the available data  [18, 44, 45]. Nevertheless, among the limits of semantic modeling, resolution of the heterogeneity of data sources, particularly semantic resolution, and mapping between these sources are very complex tasks. Furthermore, based on the OWL language, the ETL model can be redefined and reused during different stages of a DW design; however, this solution applies only to relational databases and does not support the semi-structured and unstructured data that the DW can receive. Indeed, according to  [108] , "In separated operational data sources, the syntax and semantics of data sources are extremely heterogeneous.
The intrinsic challenges include, but are not limited to, the small and diverse sizes of vehicles, inter-class similarity, illumination variation, and background complexity  [10, 11]. A simple method to combine the localization and classification score to mutually guide the training process, recently introduced by Zhang et al. [4] , has shown effectiveness in alleviating the task misalignment problem on generic computer vision datasets MS-COCO  [12]  and PASCAL-VOC  [13]. Its ability to cope with the intricacies of remote sensing vehicle detection yet remains unexplored. In this paper, we propose a framework inspired by the mutual guidance idea  [4]  for vehicle detection from remote sensing images (Figure  1 ). The idea is that the intersectionover-union (IOU) of an anchor box should contribute toward the predicted category and vice versa; the learned semantic information could help in providing more fitting bounding boxes.
The differences are that the contrastive pairs are drawn from object-instance level, not image level, and that contrastive loss is employed as an auxiliary loss in combination with the mutually guided detection loss. The contributions of the paper are fourfold, i. e. , • applying the mutual guidance idea to a remote sensing context; • formulating supervised contrastive learning as an auxiliary loss in a detection problem, which, to the best of our knowledge, is the first approach using supervised contrastive learning for object detection, especially in the context of Earth observation; • improving existing detection networks for vehicle detection by combining mutual guidance and contrastive learning, termed contrastive mutual guidance or CMG; • providing new state-of-the-art results on benchmarked datasets including VEDAI (aerial images)  [21]  and xView (satellite images)  [22].
Setup In this section, the proposed modules are analyzed and tested using the YOLOX small (-s) and medium (-m) backbones, which are adopted exactly from the YOLOv5 backbone and its scaling rules, as well as the YOLOv3 backbone (DarkNet53+SPP bottleneck) due to its simplicity and broad compatibility, and hence popularity, in various applied domains. More detailed descriptions can be referred to in the YOLOX paper  [42]. We also perform an ablation study to analyze the effects of different components and a comparative study with state-of-the-art detectors including EfficientDet  [43] , YOLOv3  [38] , YOLO-fine  [28]  YOLOv4, and Scaled-YOLOv4  [44]. For fair comparison, the input image size is fixed to 512 × 512 pixels for all experiments. Dataset. We use the VEDAI aerial image dataset  [21]  and xView satellite image dataset  [22]  to conduct our experiments.
For VEDAI, there exist two RGB versions with 12. 5-cm and 25-cm spatial resolutions. We name them as VEDAI12 and VEDAI25, respectively, in our experimental results. The original data contain 3757 vehicles of 9 different classes, including car, truck, pickup, tractor, camper, ship, van, plane, and others. As done by the authors in  [28] , we merge class plane into class others since there are only a few plane instances. Next, the images from the xView dataset were collected from the WorldView-3 satellite at 30-cm spatial resolution. We followed the setup in  [28]  to gather 19 vehicle classes into a single vehicle class. The dataset contains a total number of around 35,000 vehicles. It should be noted that our intention to benchmark these two datasets is based on their complementary characteristics.
The VEDAI dataset contains aerial images with multiple classes of vehicles from different types of backgrounds (urban, rural, desert, forest, etc. ). Moreover, the numbers of images and objects are quite limited (e. g. , 1200 and 3757, respectively). Meanwhile, the xView dataset involves satellite images of lower resolution, with a single merged class of very small vehicle sizes. It also contains more images and objects (e. g. , 7400 and 35,000, respectively). Metric. We report per-class average precision (AP) and their mean values (mAP) following the PASCAL VOC  [13]  metric. An intersection-over-union (IOU) threshold computed by the Jaccard index  [39]  is used for identifying positive boxes during evaluation. IOU values vary between 0 (no overlapping) and 1 (tight overlapping). Within the context of vehicle detection in remote sensing images, we follow  [28]  to set a small threshold, i.
Mutual Guidance Meets Contrastive Learning The results of YOLOX with the mutual guidance strategy and contrastive learning are shown in Table  3. Contrastive loss shows great benefit to the network when the misalignment between localization and classification is alleviated by mutual guidance. The improvement seems balanced between both backbones. Although the inside contrastive loss seems to dominate over the outside one in the previous experiment, it becomes inferior when the semantic information from the classification branch and projection head is properly utilized in the localization process, conforming to the finding from  [17]. The combination of mutual guidance and outside contrastive loss is coined contrastive mutual guidance, or CMG. Multiple datasets. We further show the results on different datasets with different resolutions in Table  4  and the corresponding precision-recall curve in Figure  4.