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-**Department news\\**
+**Department news**\\
 **Presentation (discussion):**
+**Title**
+An overview of my previous research efforts.
+During the talk, I (Mohamed Khalefa) mainly present my PhD thesis work which builds an integrated system to compute preference functions in efficient manner over incomplete and uncertain data.
+The PhD defense abstract is given below. Moreover, I will present other research projects I have been conducted while being a member in data management lab in University of Minnesota.
+**PhD Defense Abstract**:
+ With the increasing availability of various data sources, the
+preference queries are essential to find the relevant results to users.
+Several preference functions has been introduced in literature including:
+top-k, skylines, distributed skyline, spatial skyline, multi-objective,
+k-dominance, k-frequency, ranked skylines, k-representative dominance,
+distance-based dominance, epsilon-skylines, top-k dominance, and stochastic skyline.
+With the growing number of applications that generate imprecise data, e.g.,
+sensor readings, human reading errors, and data imperfection, it has become
+essential to support preference queries of various types over imprecise
+data. Imprecise data can be classified into two categories: incomplete and
+uncertain data.
+Unfortunately, existing work for preference queries for the imprecise data
+are limited and isolated. This thesis addresses efficiently extending DBMS
+to be preference-aware over imprecise data. First, we address the problem
+of skyline queries over incomplete data where multi-dimensional data items
+are missing some values of their dimensions. We show that with incomplete
+data, the dominance relation among data points may not be transitive, thus,
+almost all existing techniques for skyline queries are not applicable. We
+propose an efficient algorithm to compute the skyline over incomplete data.
+Then, we define preference queries over uncertain data, represented as a
+continuous range. We propose a novel, efficient framework to answer these
+preference queries. Then, we present PrefJoin, an efficient
+preference-aware join query operator, designed specifically to deal with
+preference queries where the set of preferred attributes reside in more
+than one relation. The main idea of PrefJoin is to make the join operator
+aware of the required preference functionality. Finally, we extend PrefJoin
+framework to realize an efficient preference-aware operator which supports
+imprecise data.
+**Keywords**:  Preference functions, Imprecise data, Uncertain data, Query optimization, Query Processing
 **Attendance:**
+  * Andreas Weisberg
+  * Benjamin Krogh
+  * Laurynas
+  * Christian Thomsen
+  * Kasper Søe Luckow
+  * Hua Lu
+  * Mohamed Khalefa
+  * Rene Hansen
+  * Saulius
+  * Simonas Saltenis
+  * Torben Bach Pedersen
+  * Ove Andersen
+  * Kurt Nørmark
+  * Liu Xiufeng
+  * Yoann Pitarch