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Jessica Qiu

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Top Stories by Jessica Qiu

In Java development, the typical data computation problems are characterized with: Long computation procedure requiring a great deal of debugging Data may from database, or Excel/Txt Data may from multiple databases, instead of just one. Some computation goals are complex, such as relative position computation, and set-related computation Just suppose a sales department needs to make statistics on the top 3 outstanding salesmen ranking by their monthly sales in every month from Jan to the previous month, based on the order data. Java alone is difficult to handle such computations. Although it is powerful enough and also quite convenient in debugging, Java has not directly implemented the common computational algorithms yet. So, Java programmers still have to spend great time and efforts to implement the details like aggregating, filtering, grouping, sorting, and ran... (more)

Visualized Calculation by Empowering Spreadsheet with SQL Ability

Spreadsheet software is widely used by people in every industry with flexibility for data computing and analysis. But due to inherent drawbacks, common business spreadsheet software can't conduct relational query like SQL. The spreadsheet can implement the visualized calculation to some extent, and the nontechnical people can perform some rather complex calculations without having to learn the SQL. However, as the core of SQL, the relational query is unable to be implemented through common business spreadsheet software, which adds complexity to the apparently simple problems of mu... (more)

Which Tool Will You Choose for Java: Hibernate, esProc, SQL, iBATIS or R?

The data computation layer in between the data persistent layer and the application layer is responsible for computing the data from data persistence layer, and returning the result to the application layer. The data computation layer of Java aims to reduce the coupling between these two layers and shift the computational workload from them. The typical computation layer is characterized with below features: Ability to compute on the data from arbitrary data persistence layers, not only databases, but also the non-database Excel, Txt, or XML files. Of all these computations, the... (more)

Parallelism Enhances Oracle Data Import Speed

Recently, we skillfully handled an industry project to import a great amount of data from file to Oracle in comparatively short time. At the beginning, we tried to import the data with the sqlldr of Oracle, only to find it is surprisingly time-consuming to load a great amount of data: Too slow if 2.5 hours have to be spent to import a table containing 80 million records. Later, by adopting the parallel import of sqlldr, the time is shortened to 0.8 hour. Here are the full details: Train of thoughts Split the data file to import into 10 shares. Then, with the multi-task paralleli... (more)

Data Environments Support of esProc Makes Statistical Computing More Flexible

Enterprises always have various data sources, for instance, CRM system may use SQL Server, sales reports adopt Excel, ERP applies Oracle database. When it comes to actual business analysis, enterprises usually need to conduct interactive computation, including filter, group, etc among various data environments. But data Interaction between multiple data sources are not easy to realize with some traditional statistical computing tools. In order to solve such kind of problems, esProc which adapts to various data environments comes into being. Support of various data sources is an... (more)