## 3 Literature Review
#### Homogeneous
### Mashup Integration  
Web mashups offer a lot of potential to both consumers and enterprises [16]. One of the most noted advantages of mashups is that people with little technical expertise can easily build new web applications and create new forms of visualizations [38]. However, there remains a handful of technical challenges that need to be overcome before these benefits can be realized. This section looks at cross communication from a compatibility and syntactical perspective. When mashups aggregate heterogeneous data sources it becomes difficult to convert, condense, and intelligibly communicate the summarization on a common web interface [21, 23]. An example given would be combining XML with output from a legacy COBOL system. In this situation semantically predicting relevant mashups would be a challenge given that output from the latter of the two systems contains little or no metadata. While the following section covers semantic understanding of heterogeneous data types (Mashup Agents), this section covers the conversion aspect of this problem and has been labeled 'Mashup Integration' and includes topics such as integration, transformation, and cleansing. Mashup integration is very similar to data integration, which has been well addressed in IS literature (e.g. [22, 72-75]). While much of the data integration work was published well before the term 'web mashup' was coined, this literature does provide a foundation for the problem currently being  
Figure 1: Google Cross-communication Warning [17]. experienced in mashup integration. Two competing data integration models are Local as View (LAV) and Global as View (GAV) [72, 75]. The LAV approach is based on the enterprise model, and states that content from each source should be characterized in terms of a view over the global schema [75]. An example of the LAV approach in practice is a data integration system that is based on the enterprise model [76]. \"This approach is effective whenever the data integration system is based on a global schema that is stable and well-established in the organization\" [74, pg. 235]. Therefore, the LAV approach would be suitable for enterprise mashups domains. Conversely to LAV, GAV is based on the concept that the content from each source is treated as a view which is aggregated to a global schema [74]. Each source (or view) has it's own query interface, therefore GAV would be a good integration approach for consumer mashups, that aggregate sources from multiple organizations. The aforementioned integration approaches are good solutions for mashups that are aggregating database or data warehouse's, but may not be warranted for aggregating XML sources. [PERSON] _et al._[23] propose an integration framework for XML that takes a transformation approach. Since mashups rely heavily on user interaction, the user should be included in the process when multiple sources are being transformed to a summary form [23]. They provide a \"fuser\" that takes multiple XML documents or portions of XML documents and merges them into one, from there the user can make incremental refinements to the transformation process, until a sufficient solution is developed. [PERSON] _et al._[21] present a similar transformation integration process, but add an additional emphasis on first cleaning the data before it is transformed. While data integration literature provides a good foundation for web mashups, much of this literature is limited to homogeneous integrations. For example, the LAV and GAV integration approaches discussed previously focus on coupling databases together. Even if the databases being integrated are different (e.g. Oracle and SQL Server), this is still considered a homogenous integration because the systems being coupled are the same type. The same is true with the XML integration methods presented by [PERSON] _et al._[21]. One area that integration literature needs to be extended for mashup domains is in the integration of heterogeneous data sources.