A Survey of UserCentric Data Warehouses: From Personalization to Recommendation”, The

ABSTRACT Providing a customized support for the OLAP brings tremendous challenges to the OLAP technology. Standing at the crossroads of the preferences and the data warehouse, two emerging trends are pointed out; namely: (i) the personalization and (ii) the recommendation. Although the panoply of the proposed approaches, the user-centric data warehouse community issues have not been addressed yet. In this paper we draw an overview of several user centric data warehouse proposals. We also discuss the two promising concepts in this issue, namely, the personalization and the recommendation of the data warehouses. We compare the current approaches among each others with respect to some criteria

Direct Neighbor Search

In this paper we study a novel query type, called direct neighbor query. Two objects in a dataset are direct neighbors (DNs) if a window selection may exclusively retrieve these two objects. Given a source object, a DN search computes all of its direct neighbors in the dataset. The DNs define a new type of affinity that differs from existing formulations (e.g., nearest neighbors, nearest surrounders, reverse nearest neighbors, etc) and finds application in domains where user interests are expressed in the form of windows, i.e., multi-attribute range selections. Drawing on key properties of the DN relationship, we develop an I/O optimal processing algorithm for data indexed with a spatial access method. In addition to plain DN search, we also study its K-DN and all-DN variants. The former relaxes the DN condition – two objects are K-DNs if a window query may retrieve them and only up to K − 1 other objects – whereas the all-DN variant computes the DNs of every object in the dataset. Using real, large-scale data

Personalizing queries based on networks of composite preferences

People's preferences are expressed at varying levels of granularity and detail as a result of partial or imperfect knowledge. One may have some preference for a general class of entities, for example, liking comedies, and another one for a fine-grained, specific class, such as disliking recent thrillers with Al Pacino. In this article, we are interested in capturing such complex, multi-granular preferences for personalizing database queries and in studying their impact on query results. We organize the collection of one's preferences in a preference network (a directed acyclic graph), where each node refers to a subclass of the entities that its parent refers to, and whenever they both apply, more specific preferences override more generic ones. We study query personalization based on networks of preferences and provide efficient algorithms for identifying relevant preferences, modifying queries accordingly, and processing personalized queries. Finally, we present results of both synthetic and real-user experiments, which: (a) demonstrate the efficiency of our algorithms, (b) provide insight as to the appropriateness of the proposed preference model, and (c) show the benefits of query personalization based on composite preferences compared to simpler preference representations. © 2010 ACM