# Smart Mobility with Big Data: Approaches, Applications, and Challenges
## 3 Methods and Technologies for Smart Mobility
### Big Data Framework  
Many big data frameworks are available to store and analyze the growing amount of big data. In this section, we discuss approaches to architectures that effectively store and analyze smart mobility big data using various big data frameworks, such as Apache Hadoop and Apache Spark. * MapReduce is a software framework released in 2004 by Google for distributed parallel computing and large-capacity processing consisting of a mapper and a reducer. The mapper uses a key-value pair to make sorting and grouping advantageous. MapReduce undergoes the process of inputting, splitting, mapping, shuffling, reducing, and outputting. When data are entered during the input process, they are split (splitting process) and stored in the Hadoop Distributed File System (HDFS), and the map function results are combined during the shuffling process, which is the intermediate stage of the mapping and reducing tasks. Finally, in the reducing step, all values are integrated to output the results. * Apache Hadoop is an open-source big data processing framework. It was developed to process and store increasing amounts of data, given the growth of search engines such as Yahoo and Google, and to provide users with a fast response speed. Starting with the HDFS and MapReduce framework, it expands and develops in the sense that it includes the entire Hadoop ecosystem, including data storage, execution engine programming, and data processing. * Apache Spark is a unified big data analysis framework with various built-in modules, such as SQL, streaming, machine learning, and graphing. It can run on Apache Hadoop, Apache Mesos, and Kubernetes. The SQL component can show tables such as relational databases (RDBs) and queries in SQL style. The streaming component can apply real-time data to applications. The machine learning component, MLlib, includes various machine learning models that users can use easily to edit hyperparameters. Finally, the graph component, GraphX, is Apache Spark's API for graph and graph-parallel computation. It is flexible and works seamlessly with both graphs and collections. In addition, GraphX comes with a variety of graph algorithms. It competes with the fastest graph systems in terms of performance while retaining Spark's flexibility, fault tolerance, and ease of use. * Apache Kafka is an event streaming platform widely used to collect, process, and store streaming events or general data without a separate start or end. In addition, it is an open-source distributed event streaming platform for high-performance data pipelines, data integration, and mission-critical applications. The authors of [39] developed Marmot, an extension of the existing Apache Hadoop, to facilitate the implementation of geospatial big data applications in the MapReduce framework. Marmot is an architecture that processes geospatial big data in parallel using MapReduce. Marmot's data structure closely matches the relational database management system (RDBMS), where a record in Marmot corresponds to a record in the database system, with the smallest unit of data elements comprising one or more column values in a table. Marmot has two types of RecordSetFunctions: spatial and non-spatial operators. Spatial operators input, analyze, and output spatial data. Marmot exhibited superior execution speed compared to other models, including the existing spatial Hadoop. As reported in [40], text recognition architectures existed in the past but were limited by factors such as slow speed and low accuracy due to high computational volume. Therefore, a study was conducted to improve text recognition accuracy with a low computational volume using MapReduce and Apache Hadoop to recognize the unique code written on the container. The proposed method first recognizes the unique code of the container with a camera and stores it in HDFS. Second, it converts it to a 20 \(\times\) 20 gray image. Third, the text is recognized by reducing each node's computation volume using MapReduce optical character recognition (OCR).