InetSoft Product Information: Avoiding Data Warehousing Issues with an Operational BI Solution

Would you like to avoiding data warehousing issues by using a business intelligence and data integration platform that lets you mashup disparate operational data source for OLAP reporting? Evaluate InetSoft's Style Intelligence application. Read articles below for more information.

Potential Problems with Data Mashups - What would you say about the potential problems with mashups? If we’re talking about weakness about mashups, one weakness is that I have seen in the publishing capabilities of a mashup or a mashup product. How do we go from conveying this wonderful set of information on the person’s computer screen, which is essentially a 3D world view to producing that as a hardcopy. There’s no click-through capabilities. You’re going to lose a certain amount of information. I think that really is, from what I’ve seen, is a challenge for the actual developer to take that information that we have in mashup form and incorporate that into the hardcopy world. My thoughts are around the semantic challenges. You have some users who sit in very narrow silos and understand a little bit of data, but with a mashup obviously you are bring together disparate data and creating a whole picture, and you have other users who sit at a much higher level and see it more broadly but don’t understand all the nuances that are in there. So how do we address this semantic challenge of understanding the data that we are bringing together sufficiently so there are no issues about interpretation when the mashup is rendered to them...

Click this screenshot to view a five-minute demo and get an overview of what InetSoft’s BI dashboard reporting software, Style Intelligence, can do and how easy it is to use.

Query Columns - In some cases you may need to calculate the data you want to display from existing query columns. To do this, modify the formula by placing '=' in front of the expression string. For example, consider the formula table described previously (Referencing a Query Column). To merge the 'state' and the 'zip' fields into a single cell, separated by a comma (e.g., NJ, 08901), use the following formula q['=state + ", " + zip']; You can filter out records of a field (column) based on the values of other fields in the result set. To do this, use '@' as the delimiter between the column name and the filtering expression and ':' to introduce the values to filter. For example, consider the formula table described previously (Referencing a Query Column). To extract all the companies within a certain state (NJ), you can adapt the formula as follows: q['company_name@state:NJ']; To filter based on multiple fields, use ';' as the delimiter between the filtering expressions. For example, to find all the companies within a certain city (New Brunswick) and state (NJ), adapt the formula as follows...

Querying Java Object Data Sources - Once a data source is defined, the data source can be used to create object queries. The query building process is identical to the process for XML queries, as the output of the java objects is mapped to hierarchical meta-data and can be selected and filtered using the same mechanism as all other hierarchical data sources. A query based on the object data source is executed in the following sequence: 1. A data loader object is instantiated if none have been created yet. 2. The request method is invoked with the parameter values either from the data source definition or as were input by the user. 3. The collection of object return values is parsed into an object tree based on introspection. 4. Any user defined filtering and selection in the query is applied to produce the final result set...

Query Compatibility - You can dynamically change the data binding of an element from within script by setting the “query” property. Query name, such as grouping and summary fields, and formula columns. If you change a query from within script, you must make sure that the new query is compatible with these other specifications. For Table and Chart elements, a query is considered compatible if it contains the same grouping and summary columns specified in data binding. Since the table can adjust itself to accommodate any tabular data, no change in table layout is necessary. Because Section elements have fixed layout, the compatibility rules are stricter. If you use script to change the query binding for a Section, the new query must contain exactly the same columns as the query that it replaces. Otherwise, the Section will not display the new columns and will use default values for all columns that are missing in the new query. In practice, dynamic query binding is most useful when your reports have tables with the exact same columns, but with different conditions or data sources. Keep in mind however, that Report Beans are the preferred way to achieve component-based reporting and report reuse...

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