Figure 1 shows a portion of a relational table contained in a real, large information system. The table concerns the customers of an organization, where each row stores data about a single customer. The first column contains her code (if the code is negative, then the record refers to a special customer, called “fictitious”), columns 2 and 3 specify the time interval of validity for the record, ID_GROUP indicates the group the customer belongs to (if the value of FLAG_CP is “S”, then the customer is the leader of the group, and if FLAG_CF is “S”, then the customer is the controller of the group), FATTURATO is the annual turnover (but the value is valid only if FLAG_FATT is “S”). Obviously, each notion mentioned above (like “fictitious”, “group”, “leader”, etc.) has a specific meaning in the organization, and understanding such meaning is crucial if one wants to correctly manage the data in the table and extract information out of it. Similar rules hold for the other 47 columns that, for lack of space, are not shown in the figure.
Figure 1: A portion of the Customer table in a database of a large organization.
Those who have experience of large databases, or databases that are part of large information systems will not be surprised to see such complexity in a single data structure. Now, think of a database with many tables of this kind, and try to imagine a poor final user accessing such tables to extract useful information. The problem is even more severe if one considers that information systems in the real world use different (often many) heterogeneous data sources, both internal and external to the organization. Finally, if we add to the picture the (inevitable) need of dealing with big data, and consider in particular the two v’s of “volume” and “velocity”, we can easily understand why effectively accessing, integrating and managing data in complex organizations is still one of the main issues faced by IT industry nowadays.
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