On synthetic workloads for multiplayer online games: a methodology for generating representative shooter game workloads

We present approaches to the generation of synthetic workloads for benchmarking multiplayer online gaming infrastructures. Existing techniques, such as mobility or traffic models, are often either too simple to be representative for this purpose or too specific for a particular network structure. Desirable properties of a workload are reproducibility, representativeness, and scalability to any number of players. We analyze different mobility models and AI-based workload generators. Real gaming sessions with human players using the prototype game Planet PI4 serve as a reference workload. Novel metrics are used to measure the similarity between real and synthetic traces with respect to neighborhood characteristics. We found that, although more complicated to handle, AI players reproduce real workload characteristics more accurately than mobility models.     

First-order query rewriting for inconsistent databases

We consider the problem of retrieving consistent answers over databases that might be inconsistent with respect to a set of integrity constraints. In particular, we concentrate on sets of constraints that consist of key dependencies, and we give an algorithm that computes the consistent answers for a large and practical class of conjunctive queries. Given a query q, the algorithm returns a first-order query Q (called a query rewriting) such that for every (potentially inconsistent) database I, the consistent answers for q can be obtained by evaluating Q directly on I.     

Content-based query processing for video databases

This paper presents a query processing strategy for the content-based video query language named CVQL. By CVQL, users can flexibly specify query predicates by the spatial and temporal relationships of the content objects. The query processing strategy evaluates the predicates and returns qualified videos or frames as results. Before the evaluation of the predicates, a preprocessing is performed to avoid unnecessary accessing of videos which are impossible to be the answers. The preprocessing checks the existence of the content objects specified in the predicates to eliminate unqualified videos. For the evaluation of the predicates, an M-index is designed based on the analysis of the behaviors of the content objects. The M-index is employed to avoid frame-by-frame evaluation of the predicates. Experimental results are presented to illustrate the performance of this approach     

Logic-based query optimization for object databases

We present a technique for transferring query optimization techniques, developed for relational databases, into object databases. We demonstrate this technique for ODMG database schemas defined in ODL and object queries expressed in OQL. The object schema is represented using a logical representation (Datalog). Semantic knowledge about the object data model, e.g., class hierarchy information, relationship between objects, etc., as well as semantic knowledge about a particular schema and application domain are expressed as integrity constraints. An OQL object query is represented as a logic query and query optimization is performed in the Datalog representation. We obtain equivalent (optimized) logic queries, and subsequently obtain equivalent (optimized) OQL queries for each equivalent logic query. We present one optimization technique for semantic query optimization (SQO) based on the residue technique of U. Charavarthy et al. (1990; 1986; 1988). We show that our technique generalizes previous research on SQO for object databases. We handle a large class of OQL queries, including queries with constructors and methods. We demonstrate how SQO can be used to eliminate queries which contain contradictions and simplify queries, e.g., by eliminating joins, or by reducing the access scope for evaluating a query to some specific subclass(es). We also demonstrate how the definition of a method or integrity constraints describing the method, can be used in optimizing a query with a method     

A query algebra for program databases

Querying source code is an essential aspect of a variety of software engineering tasks such as program understanding, reverse engineering, program structure analysis and program flow analysis. In this paper, we present and demonstrate the use of an algebraic source code query technique that blends expressive power with query compactness. The query framework of Source Code Algebra (SCA) permits users to express complex source code queries and views as algebraic expressions. Queries are expressed on an extensible, object-oriented database that stores program source code. The SCA algebraic approach offers multiple benefits such as an applicative query language, high expressive power, seamless handling of structural and flow information, clean formalism and potential for query optimization. We present a case study where SCA expressions are used to query a program in terms of program organization, resource flow, control flow, metrics and syntactic structure. Our experience with an SCA-based prototype query processor indicates that an algebraic approach to source code queries combines the benefits of expressive power and compact query formulation     

Time-by-example query language for historical databases

The authors propose a graphical query language, Time-by-Example (TBE), which has suitable constructs for interacting with historical relational databases in a natural way. TBE is user-friendly. It follows the graphical, two-dimensional approach of such previous languages as Query-by-Example (QBE), Aggregation-by-Example (ABE), and Summary-Table-by-Example (STBE). TBE also uses the hierarchical window (subquery) concept of ABE and STBE. TBE manipulates triple-valued (set-triple-valued) attributes and historical relations. Set-theoretic expressions are followed to deal with time intervals. The BNF specification for TBE is given     

COACT: a query interface language for collaborative databases

Data curation activities in collaborative databases mandate that collaborators interact until they converge and agree on the content of their data. In a previous work, we presented a cloud-based collaborative database system that promotes and enables collaboration and data curation scenarios. Our system classifies different versions of a data item to either pending, approved, or rejected. The approval or rejection of a certain version is done by the database Principle Investigators (or PIs) based on its value. Our system also allows collaborators to view the status of each version and help PIs take decisions by providing feedback based on their experiments and/or opinions. Most importantly, our system provided mechanisms for history tracking of different versions to trace the modifications and approval/rejection done by both collaborators and PIs on different versions of a data item. We labeled our system as Update-Pending-Approval model (or UPA). In this paper, we describe a high level SQL query interface language for PIs and collaborators to interact with the UPA framework. We define a set of UPA keywords that are used as a part of the history tracking mechanism to select specific versions of a data item, and a set of UPA options that select specific versions based on possible future decisions of PIs. We implemented our query interface mechanism on top of Apache Phoenix, taking into consideration that the UPA system was implemented on top of Apache HBase. We test the performance of the UPA query language by executing several queries that contain different complexity levels and discuss their results.     

Object-oriented query language access to relational databases: A semantic framework for query translation

This research investigates and approach to query processing in a multidatabase system that uses an objectoriented model to capture the semantics of other data models. The object-oriented model is used to construct a global schema, defining an integrated view of the different schemas in the environment. The model is also used as a self-describing model to build a meta-database for storing information about the global schema. A unique aspect of this work is that the object-oriented model is used to describe the different data models of the multidatabase environment, thereby extending the meta database with semantic information about the local schemas. With the global and local schemas all represented in an object-oriented form, structural mappings between the global schema and each local schema are then easily supported. An object algebra then provides a query language for expressing global queries, using the structural mappings to translate object algebra queries into SQL queries over local relational schema. The advantage of using an object algebra is that the object-oriented database can be viewed as a blackboard for temporary storage of local data and for establishing relationships between different databases. The object algebra can be used to directly retrieve temporarily-stored data from the object-oriented database or to transparently retrieve data from local sources using the translation process described in this paper.     

A graphical query language for simulation databases

In this paper we propose a graphical query language to analyse simulation runs which are stored in databases with a temporal dimension. A simulation run is a sequence of system states produced by discrete simulation. It may become very large, such that manual analysis based on database browsing is no longer feasible. Hence simulation databases require a specific query language to analyse the simulation data. Queries may be related to single states (e.g. Is there a state where condition c1 holds? ) or to state sequences (e.g. Is there a state sequence, where first c1 holds, then c2 and finally c3? ).An existing Petri net simulation environment has been extended to support the described concepts.     

Rule-based spatiotemporal query processing for video databases

In our earlier work, we proposed an architecture for a Web-based video database management system (VDBMS) providing an integrated support for spatiotemporal and semantic queries. In this paper, we focus on the task of spatiotemporal query processing and also propose an SQL-like video query language that has the capability to handle a broad range of spatiotemporal queries. The language is rule-based in that it allows users to express spatial conditions in terms of Prolog-type predicates. Spatiotemporal query processing is carried out in three main stages: query recognition, query decomposition, and query execution.Received: 11 October 2001, Accepted: 3 October 2003, Published online: 12 December 2003Edited by: A. Buchmann Correspondence to: Özgür UlusoyThis work is supported by the Scientific and Research Council of Turkey (TÜBITAK) under Project Code 199E025. This work was done while the first author was at Bilkent University.     

Typed query languages for databases containing queries

This paper introduces and studies the relational meta algebra, a statically typed extension of the relational algebra to allow for meta programming in databases. In this meta algebra one can manipulate database relations involving not only stored data values (as in classical relational databases) but also stored relational algebra expressions. Topics discussed include modeling of advanced database applications involving “procedural data” ; desirability as well as limitations of a strict typing discipline in this context; equivalence with a first-order calculus; and global expressive power and non-redundancy of the proposed formalism.     

面向外包空间数据库的范围查询验证

针对空间范围查询验证方法（例如VR-tree和MR-tree）普遍存在验证对象（VO）中包含大量的节点验证信息,造成服务器到客户端的传输代价较大以及客户端验证效率较低等问题,提出一种新的验证索引结构（ADS）MGR-tree。首先利用拆分思想,通过在Grid-tree的叶子节点中嵌入R-tree,并结合Merkle哈希树的验证方法,极大地减小VO的大小,提高查询和验证的效率。在此基础上,利用Hilbert曲线降维的特性,构建了优化的索引结构MHGR-tree,并提出一种过滤策略,进一步提高验证的效率。实验结果表明,所提方法具有更好的表现。在最好情况下,MHGR的VO大小和验证时间仅为MR的63%和19%。     

Relational completeness of query languages for annotated databases

Annotated relational databases can be queried either by simply making the annotations explicitly available along the ordinary data, or by adapting the standard query operators so that they have an implicit effect also on the annotations. We compare the expressive power of these two approaches. As a formal model for the implicit approach we propose the color algebra, an adaptation of the relational algebra to deal with the annotations. We show that the color algebra is relationally complete: it is equivalent to the relational algebra on the explicit annotations. Our result extends a similar completeness result established for the query algebra of the MONDRIAN annotation system, from unions of conjunctive queries to the full relational algebra. We also show that the color algebra is nonredundant: no operator can be expressed in terms of the other operators. We also present a generalization of the color algebra that is relationally complete in the presence of built-in predicates on the annotations.     

Generating efficient safe query plans for probabilistic databases

Managing uncertain information using probabilistic databases has drawn much attention recently in many fields. Generating efficient safe plans is the key to evaluating queries whose data complexities are PTIME. In this paper, we propose a new approach generating efficient safe plans for queries. Our algorithm adopts effective preprocessing and multiway split techniques, thus the generating safe plans avoid unnecessary probabilistic cartesian-products and have the minimum number of probabilistic projections. Further, we extend existing transformation rules to allow the safe plans generated by the Safe-Plan algorithm [N. Dalvi, D. Suciu, Efficient query evaluation on probabilistic database, The VLDB Journal 16 (4) (2007) 523–544] and the proposed algorithm to be transformed by each other. Applying our approach through the TPC-H benchmark queries, the experiments show that the safe plans generated by our algorithm are more efficient than those generated by the Safe-Plan algorithm.     

Modeling and generating realistic streaming media server workloads

Currently, Internet hosting centers and content distribution networks leverage statistical multiplexing to meet the performance requirements of a number of competing hosted network services. Developing efficient resource allocation mechanisms for such services requires an understanding of both the short-term and long-term behavior of client access patterns to these competing services. At the same time, streaming media services are becoming increasingly popular, presenting new challenges for designers of shared hosting services. These new challenges result from fundamentally new characteristics of streaming media relative to traditional web objects, principally different client access patterns and significantly larger computational and bandwidth overhead associated with a streaming request. To understand the characteristics of these new workloads we use two long-term traces of streaming media services to develop MediSyn, a publicly available streaming media workload generator. In summary, this paper makes the following contributions: (i) we propose a framework for modeling long-term behavior of network services by capturing the process of file introduction, non-stationary popularity of media accesses, file duration, encoding bit rate, and session duration. (ii) We propose a variety of practical models based on the study of the two workloads. (iii) We develop an open-source synthetic streaming service workload generator to demonstrate the capability of our framework to capture the models.     

Fast query processing in deductive databases

A special-purpose algorithm, that analyzes the structure of a recursion and exploits its properties in query processing in a deductive database is presented. This method is applied to linear rules, a large and common class of recursion. The structural approach to rule processing (SARP) prototype system that implements the algorithm is described     

Skyline-join query processing in distributed databases

The skyline-join operator, as an important variant of skylines, plays an important role in multi-criteria decision making problems. However, as the data scale increases, previous methods of skyline-join queries cannot be applied to new applications. Therefore, in this paper, it is the first attempt to propose a scalable method to process skyline-join queries in distributed databases. First, a tailored distributed framework is presented to facilitate the computation of skyline-join queries. Second, the distributed skyline-join query algorithm (DSJQ) is designed to process skyline-join queries. DSJQ contains two phases. In the first phase, two filtering strategies are used to filter out unpromising tuples from the original tables. The remaining tuples are transmitted to the corresponding data nodes according a partition function, which can guarantee that the tuples with the same join value are transferred to the same node. In the second phase, we design a scheduling plan based on rotations to calculate the final skyline-join result. The scheduling plan can ensure that calculations are equally assigned to all the data nodes, and the calculations on each data node can be processed in parallel without creating a bottleneck node. Finally, the effectiveness of DSJQ is evaluated through a series of experiments.     

Probabilistic query answering over inconsistent databases

This paper presents a framework for querying inconsistent databases in the presence of functional dependencies. Most of the works dealing with the problem of extracting reliable information from inconsistent databases are based on the notion of repair, a minimal set of tuple insertions and deletions which leads the database to a consistent state (called repaired database), and the notion of consistent query answer, a query answer that can be obtained from every repaired database. In this work, both the notion of repair and query answer differ from the original ones. In the presence of functional dependencies, tuple deletions are the only operations that are performed in order to restore the consistency of an inconsistent database. However, deleting a tuple to remove an integrity violation potentially eliminates useful information in that tuple. In order to cope with this problem, we adopt a notion of repair, based on tuple updates, which allows us to better preserve information in the source database. A drawback of the notion of consistent query answer is that it does not allow us to discriminate among non-consistent answers, namely answers which can be obtained from a non-empty proper subset of the repaired databases. To obtain more informative query answers, we propose the notion of probabilistic query answer, that is query answers are tuples associated with probabilities. This new semantics of query answering over inconsistent databases allows us to give a measure of uncertainty to query answers. We show that the problem of computing probabilistic query answers is FP ^#P -complete. We also propose a technique for computing probabilistic answers to arbitrary relational algebra queries.     

Constraint-based query evaluation in deductive databases

Constraints play an important role in the efficient query evaluation in deductive databases. Constraint-based query evaluation in deductive databases is investigated, with emphasis on linear recursions with function symbols. Constraints are grouped into three classes: rule constraints, integrity constraints, and query constraints. Techniques are developed for the maximal use of different kinds of constraints in rule compilation and query evaluation. The study on the roles of different classes of constraints in set-oriented evaluation of linear recursions shows the following: rule constraints should be integrated with their corresponding deduction rules in the compilation of recursions; integrity constraints, including finiteness constraints and monotonicity constraints, should be used in the analysis of finite evaluability and termination for specific queries; and query constraints, which are often useful in search space reduction and termination, should be transformed, when necessary, and should be pushed into the compiled chains as deeply as possible for efficient evaluation. The constraint-based query-processing technique integrates query-independent compilation and chain-based query evaluation methods and demonstrates its great promise in deductive query evaluation     

Efficient query evaluation on probabilistic databases

We describe a framework for supporting arbitrarily complex SQL queries with “uncertain” predicates. The query semantics is based on a probabilistic model and the results are ranked, much like in Information Retrieval. Our main focus is query evaluation. We describe an optimization algorithm that can compute efficiently most queries. We show, however, that the data complexity of some queries is #P-complete, which implies that these queries do not admit any efficient evaluation methods. For these queries we describe both an approximation algorithm and a Monte-Carlo simulation algorithm.