Complex xsd file

Oracle Data Integrator ODI is a metadata driven ETL tool and to build your data flows you need to know about the source and target for the data transformation. How does it get there?

This is done through the process of reverse engineering, creates corresponding datastores and relationships inside an ODI model. XML is no exception. When ODI accesses the XML file using its XML driver for the first time, it analyses the structure of the associated schema and creates corresponding structures with tables and relationships. We are going to show you how this is done by using the in-memory engine.

We will now walk you through an example of reverse engineering an XSD and give you plenty of tips along the way.

We will use the same SEPA pain schema as in this post and share some tips along the way. Just to remind you, Pain. Find out more about PAIN. In a first step we create the physical data server. As we will use the in-memory mode the whole External DB properties section is not relevant. This would be the first tag that comes in the file, containing all other tags in it.

We start by defining the "shipto" element:. With schemas we can define the number of possible occurrences for an element with the maxOccurs and minOccurs attributes. The default value for both maxOccurs and minOccurs is 1! Now we can define the "item" element.

This element can appear multiple times inside a "shiporder" element. This is specified by setting the maxOccurs attribute of the "item" element to "unbounded" which means that there can be as many occurrences of the "item" element as the author wishes. Notice that the "note" element is optional.

We have specified this by setting the minOccurs attribute to zero:. We can now declare the attribute of the "shiporder" element. The previous design method is very simple, but can be difficult to read and maintain when documents are complex. Add a comment. Active Oldest Votes.

Notes You'll want to include Student. See also What's the difference between xsd:include and xsd:import? How to reference element in other XSD's namespace? Improve this answer. I need to reference complex types from other namespace as well. Can you help me with the code for that? I've added a link to a complete, working example of how to use xsd:import. I am getting this error while trying to reference complex type student in teacher "Invalid attribute value for 'ref' in element 'element'.

Recorded reason: UndeclaredPrefix: Cannot resolve 'student:student' as a QName: the prefix 'student' is not declared".

Can you please check? Thus, the author defines both the tags and the document structure. XML stores data in plain text format, making it human-readable and machine-readable.

This provides an independent way of storing, transporting, and sharing data. Elements can be simple or complex. A simple element can contain only text, but they can be of several different types, such as boolean, string, decimal, integer, date, time, and so on. By contrast, a complex element contains other simple or complex types W3C, Complex XSD is considered in several studies Krishnamurthy et al.

The complex contents specify that the new type will have more than one element. Extension bases refer to the creation of new data types that extend the structure defined by other data types W3C, Appendix A presents an example of a complex XSD that follows the 3rd Generation Partnership Project format used for performance management in mobile networks. In this work, the files used for the case study are based on this XSD. The XSD can also define attributes that contain data related to a specific element.

As best practice, attributes are used to store metadata of the elements and the data itself are stored as the value of the elements W3C, The XML syntax below presents an example of the use of attributes and values in the elements: an attribute is used to store the identification number of the element and a value is used to store the data.

Apache Hive fundamentally works with two different types of tables: internal managed and external. With the use of internal tables, Apache Hive assumes that the data and its properties are owned by Apache Hive and can only be changed via Apache Hive command; however, the data reside in a normal file system Francke, Thus, when the external table is dropped, only the schema in the database is dropped, not the data Francke, Apache Spark Apache, c is an engine for large data processing.

These libraries can be combined in the same application. This tool also runs on HDFS. An Apache Spark data frame is a data set organized into named columns, similar to a table in a relational database.

An RDD is a collection of elements partitioned across the nodes of the cluster that can be operated in parallel. The mobile network sector is one of the fastest-growing industries around the world.

In summary, PM data check the behavior of network traffic, almost in real time, through the values of the measurements transmitted in every file. Each vendor then adapts and personalizes them according to their needs.

A review of the literature identified research related to querying XML documents that involves numerous methods and algorithms. In the following section, we review the research most closely related and relevant to our study and explain how these studies differ from our approach. This comparison highlights some of the contributions of our work. Hricov et al. The XML files contain only four attributes.

The main difference from our study is that we evaluate the query execution times for XML documents of complex types from real-life mobile networks in Apache Hive and Apache Spark. Furthermore, we explain in detail how to apply our proposed method to facilitate its replication in other studies, whereas Hricov et al. Every tag is stored in the markup column, content refers to the value of the attribute and URI the data location. In contrast to our research, the cited study does not present examples for XSD with complex types, neither the results of the computation times in Apache Hive and Apache Spark.

Moreover, the cited study only presents an approach for external tables, whereas our study includes internal tables and data frames for Apache Spark. They test the result times for streaming and batched query. They stated that the XML files need to be parsed and then indexes are produced to be stored as HDFS files; however, no details about the method employed to process the data is described, nor the XSD used in the research. They present the execution times obtained for the index construction and query evaluation.

However, we explain in detail how to implement our approach using real data sets from mobile networks for Apache Hive and Apache Spark. For tests, they employ a web-based virtual collaboration tool called VCEI. For each session, an XML format file is generated with four main entities: identification, opinion, location in the image, and related symbols.

In this file, the opinions of the users are associated with digital images. A single table is created with the generated XML document. This work presents an example with an XML document. This approach focuses on providing a query interface for flight and weather data integration, but unlike our study does not evaluate query time execution in Apache Hive and Apache Spark. Apache Spark. However, no detail about the implementation is provided nor is there any information on the XSD used and the behavior for Apache Hive and Apache Spark that we consider in our study.

They thus propose a detailed procedure to design XML schemas. They state that the Apache Hive XML serializer-deserializer and explode techniques are suitable for dealing with complex XML and present an example of the creation of a table with a fragment of a complex XML file. By contrast, in our work, we propose the cataloging procedure and also evaluate our approach for Apache Spark data frames. We additionally present the query execution times obtained after applying our proposal.

Finally, our research explains how to use cataloging, deserialization and positional explode to process complex XSD in Apache Hive internal and external tables and Apache Spark data frames; moreover, we demonstrate the validity of our proposal in a test Big Data environment with real PM XML files from two mobile network vendors. Figure 1 sketches the architecture employed to evaluate the query execution times for Apache Hive and Apache Spark.

In the extract phase presented in Fig. Then, in the load phase, these data are stored in a repository such as HDFS. No changes in the format of the XML files are made in the load process; therefore the transformation process is not comprised. Our main goal is to facilitate the acknowledgment of XML element types to create tables and perform queries in both Apache Hive and Apache Spark. Root type corresponds to the main tag used to initiate and terminate the XML file.

Array and Structure types relate to the types of elements or children-elements array and structure, respectively. Attribute refers to the attributes and Value denotes the values of elements that can be of different types, such as strings, char, short, int, float, among others.

For clarity, we present an example of the identification of vectors and its respective catalog for the XSD in Fig. Figure 2 presents an example of a complex XSD. This contains the following complex types: element 1, element 11, element , element , and element Furthermore, element 11 extends element , and element extends element For Fig.

A 1 is composed of one schema; thus vector X is composed by one element X 1 :. Figure 3 summarizes the workflow for the three main methods: 1 cataloging, 2 deserialization, and 3 positional explode. An index is identified for each array.