Preface

Information explosion and advances in computing hardware have brought forth a new generation of applications on the Internet and on mobile devices that are poised to transform the way we work and play. One of the biggest challenges for the database community is to better support the ubiquity of big data in the Internet age. This calls for new data management solutions that traditional DBMSs cannot provide. The JCST Special Section on Trends Changing Data Management aims at bringing together researchers in data management to discuss the state of the database research and its impacts on practice.
The special section has received enthusiastic responses. The topics of submitted papers ranged from flash memory databases to cloud databases. After careful review, we have accepted 13 papers, each of which has high technical quality and collectively cover a wide range of topics that reflect new trends in data management.
The paper "Outlier Detection over Sliding Windows for Probabilistic Data Streams'' by Wang \it et al. addresses the challenges inherent in the probabilistic or the uncertain data. It studies a classic problem --- outlier detection --- in the new setting.
The paper "Privacy-Preserving Data Sharing in Cloud Computing'' by Hui Wang addresses two important problems that are characteristic of the Internet age. Cloud computing is poised to revolutionize IT, and it also represents a controversial concept because of the disruptive change it may incur in the area of privacy. The paper is one of the first that addresses this concern.
The paper "Efficient Location Updates for Continuous Queries over Moving Objects'' by Hsueh \it et al. addresses challenges in location databases. The wide adoption of mobile devices spurred many applications in this domain, and the paper sheds light on advances in processing location aware queries.
The paper "Towards Progressive and Load Balancing Distributed Computation: A Case Study on Skyline Analysis'' by Huang \it et al. addresses another reality in the new trends of data management: data distribution and load balancing. The paper proposed a solution known as progressive query processing for this task.
The paper "Context-Sensitive Document Ranking'' by Chang \it et al. focuses on an important data management issue on the Web: ranking. The paper focuses on using auxiliary information associated with documents to improve ranking.
The paper "The Inverse Classification Problem'' by Aggarwal \it et al. focuses on data mining, which is the cornerstone technique in handling the big data. The paper proposed a very interesting problem --- determining attribute values given class labels --- that may benefit many applications.
The paper "Annotation Based Query Answer over Inconsistent Database'' by Wu \it et al. focuses on query processing against inconsistent database. It addresses one of the current challenges in data management, i.e., data does not observe constraints, such as functional dependency, required by a traditional DBMS.
The paper "HAPS: Supporting Effective and Efficient Full-Text P2P Search with Peer Dynamics'' by Ren \it et al. shows that much data is being stored in P2P systems, where data management issues abound. Specifically, it studies how to improve search in a P2P environment.
The paper "A Solution of Data Inconsistencies in Data Integration --- Designed for Pervasive Computing Environment'' by Wang \it et al. addresses data inconsistency issues in data integration. The approach it proposes uses data quality as a criterion.
The paper "Flash-Optimized B+-Tree'' by On \it et al. studies issues of data access in flash memory databases. As flash memory has increasing data storage capacity, the database community is faced with the challenge of re-optimizing data accesses for memory based instead of disk based devices.
The paper "Efficient Distributed Skyline Queries for Mobile Applications'' by Xiao \it et al. focuses on query processing in mobile environment. It deals with a wide range of issues including mobility, wireless bandwidth, disconnections, etc.
The paper "A Query Interface Matching Approach Based on Extended Evidence Theory for Deep Web'' by Dong \it et al. studies a schema matching like problem in deep Web, i.e., how to match query interfaces specified by Web forms so that the users can tap into the rich data hidden inside the deep Web.
The paper "Dynamic Damage Recovery for Web Databases'' by Zhu \it et al. investigate a problem familiar to the database community --- data recovery, except that the database is web based.
We thank our authors and reviewers who contributed greatly to the special section. We are proud of its high quality papers and its diverse topics. We hope that the vision, the technical achievements, and the empirical findings as presented in this special section will help encourage the database community to address the challenges in the new trends of data management.