# Application of Workflow Technology for Big Data Analysis Service
## 1 Introduction
This analysis system provides ETL, data mining, OLAP, and reporting services as cloud services via the Internet. The core of DDM is the workflow system. Users rend and customize a series of big data analysis applications. DDM transforms users' requirements into workflow processes, which can be allocated as parallel task sequences in cloud computing platforms. Traditional workflow management systems are commonly developed and deployed within enterprises for employees' collaboration works. Enterprise workflow systems are inappropriate for big data analysis and multi-user high-concurrency situations. Enterprise workflow systems act as Internet applications that may meet some problems, including the following:  
1. Multi-user high concurrency: Generally, the number of users for an enterprise workflow system is a few hundred to several thousand, whereas visits to the Internet often reach millions. Enterprise workflow systems have high rates of system resource occupation, which cannot match with lightweight SaaS applications. 2. Big data analysis: Traditional workflow systems usually run within memories or databases. During the running process, the workflow status is stored in memory; accordingly, the system memory requirements are proportional to the number of workflows. The number of workflows increases rapidly with the number of users. Traditional workflow systems cannot satisfy the requirement that the number of workflows is unlimited. 3. Complex business process: Workflows of DDM are big data analysis tasks rather than collaborative office among employees. Big data analysis tasks can usually be divided into several analysis sub-tasks that involve analysis nodes. The interactions among sub-tasks are complex, such as data- or control-dependent. Big data analysis tasks allow more than one starting node, and the priority to sub-tasks can be configured. During the data analysis process, analysis nodes need to be added to workflows dynamically according to the intermediate result. The addition of nodes monitors the execution statuses of tasks and processes them constantly because big data analysis is time consuming. Although the application of workflow in the cloud computing field has proliferated in recent years, efforts in data analysis--especially big data analysis--are lacking. [PERSON] et al. [10] investigated the possibility of integrating business process management into cloud computing. Cordys Process Factory [11] was especially designed as cloud workflow for Google Apps, allowing users to design business application programs on a web browser. Cordys Process Factory provides a structured workflow system into the unsystematic set of Google Apps--that is, a rearrangement of applications and application workflows. IBM introduced LotusLive [12], which provides online business collaboration and social network services. Users can obtain computing capacity via web portals. Although LotusLive was designed as a SaaS model, it mainly provides online business collaboration and social network services. LotusLive is highly coupled with its provided services. This study proposes a representational state transfer (RESTful)-based lightweight cloud workflow system to solve the aforementioned problems and support big data analysis of SaaS. The system advantages include the following:  
1. Our system provides web-based parallel big data analysis flow tools. Users can drag and drop components that involve parallel data analysis algorithms to create a big data analysis workflow. They can configure algorithm parameters and cloud resources according to their needs. Rich visualization components are offered to show the results. 2. A lightweight cloud workflow mechanism that can parse workflows into Extensible Markup Language (XML) description documents and directed acyclic graphs (DAGs) is designed. The workflow mechanism uses a RESTful interface to connect with the cloud platform. Workflow databases exist in HBase, which store the parsed XML documents and DAGs.