Domain analysis aims at obtaining knowledge to a particular domain in the early stage of software development. A key challenge in domain analysis is to extract features automatically from related product artifacts. Compared with other kinds of artifacts, high volume of descriptions can be collected from app marketplaces (such as Google Play and Apple Store) easily when developing a new mobile application (App), so it is essential for the success of domain analysis to obtain features and relationship from them using data technologies. In this paper, we propose an approach to mine domain knowledge from App descriptions automatically. In our approach, the information of features in a single app description is firstly extracted and formally described by a Concern-based Description Model (CDM), this process is based on predefined rules of feature extraction and a modified topic modeling method; then the overall knowledge in the domain is identified by classifying, clustering and merging the knowledge in the set of CDMs and topics, and the results are formalized by a Data-based Raw Domain Model (DRDM). Furthermore, we propose a quantified evaluation method for prioritizing the knowledge in DRDM. The proposed approach is validated by a series of experiments.

Multi-temporal InSAR technique can implement continuous earth surface deformation detection with long time scale and wide geographical coverage. In this paper, we first employ the Small Baseline Subset method to survey potential landslides in Guide County, Qinghai Province, which is identified as a loess landslide prone area for geological and climate conditions. Two anomalous deformation regions are detected by L-band Phased Array and L-band Synthetic Aperture Radar stacks. Then, qualitative and quantitative evaluations of the measuring points are given for understanding the distribution regularity of deformation. Finally, preliminary correlation between the time-series deformation and triggering factors is analyzed to explore the driving mechanism for landslide movement. The results demonstrate that L-band SAR has high potential in landslide monitoring applications and can be used as the basis for landslide recognizing, precursory information extracting, and early warning.