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Advancement in mobile computing technologies has prompted strong needs for database systems that can be used in small devices such as sensors, cellular phones, PDAs, car navigators, and Ultra Mobile PCs (UMPCs). We term the database systems that are customizable for small computing devices as Ubiquitous Database Management Systems (UDBMSs). In this paper, we first review the requirements of the UDBMS. The requirements identified include lightweight DBMSs, selective convergence, flash-optimized storage systems, data synchronization, support of unstructured/semistructured data, complex database operations, selfmanagement, and security. Next, we review existing systems and research prototypes. We review the functionality of UDBMSs including the footprint size, support of standard SQL, transaction management, concurrency control, recovery, indexing, and access control. We then review the supportability of the requirements by those UDBMSs surveyed. We finally present research issues related to the UDBMS.

We present a probabilistic algorithm for counting the number of unique values in the presence of duplicates. This algorithm has O ( q ) time complexity, where q is the number of values including duplicates, and produces an estimation with an arbitrary accuracy prespecified by the user using only a small amount of space. Traditionally, accurate counts of unique values were obtained by sorting, which has O ( q log q ) time complexity. Our technique, called linear counting , is based on hashing. We present a comprehensive theoretical and experimental analysis of linear counting. The analysis reveals an interesting result: A load factor (number of unique values/hash table size) much larger than 1.0 (e.g., 12) can be used for accurate estimation (e.g., 1% of error). We present this technique with two important applications to database problems: namely, (1) obtaining the column cardinality (the number of unique values in a column of a relation) and (2) obtaining the join selectivity (the number of unique values in the join column resulting from an unconditional join divided by the number of unique join column values in the relation to he joined). These two parameters are important statistics that are used in relational query optimization and physical database design.

We present techniques for optimizing queries in memory-resident database systems. Optimization techniques in memory-resident database systems differ significantly from those in conventional disk-resident database systems. In this paper we address the following aspects of query optimization in such systems and present specific solutions for them: (1) a new approach to developing a CPU-intensive cost model; (2) new optimization strategies for main-memory query processing; (3) new insight into join algorithms and access structures that take advantage of memory residency of data; and (4) the effect of the operating system's scheduling algorithm on the memory-residency assumption. We present an interesting result that a major cost of processing queries in memory-resident database systems is incurred by evaluation of predicates. We discuss optimization techniques using the Office-by-Example (OBE) that has been under development at IBM Research. We also present the results of performance measurements, which prove to be excellent in the current state of the art. Despite recent work on memory-resident database systems, query optimization aspects in these systems have not been well studied. We believe this paper opens the issues of query optimization in memory-resident database systems and presents practical solutions to them.