J. Schaffner, Tim Januschowski, Mary H. Kercher, Tim Kraska, H. Plattner, M. Franklin, D. Jacobs
In the cloud services industry, a key issue for cloud operators is to minimize operational costs. In this paper, we consider algorithms that elastically contract and expand a cluster of in-memory databases depending on tenants' behavior over time while maintaining response time guarantees. We evaluate our tenant placement algorithms using traces obtained from one of SAP's production on-demand applications. Our experiments reveal that our approach lowers operating costs for the database cluster of this application by a factor of 2.2 to 10, measured in Amazon EC2 hourly rates, in comparison to the state of the art. In addition, we carefully study the trade-off between cost savings obtained by continuously migrating tenants and the robustness of servers towards load spikes and failures.
{"title":"RTP: robust tenant placement for elastic in-memory database clusters","authors":"J. Schaffner, Tim Januschowski, Mary H. Kercher, Tim Kraska, H. Plattner, M. Franklin, D. Jacobs","doi":"10.1145/2463676.2465302","DOIUrl":"https://doi.org/10.1145/2463676.2465302","url":null,"abstract":"In the cloud services industry, a key issue for cloud operators is to minimize operational costs. In this paper, we consider algorithms that elastically contract and expand a cluster of in-memory databases depending on tenants' behavior over time while maintaining response time guarantees.\u0000 We evaluate our tenant placement algorithms using traces obtained from one of SAP's production on-demand applications. Our experiments reveal that our approach lowers operating costs for the database cluster of this application by a factor of 2.2 to 10, measured in Amazon EC2 hourly rates, in comparison to the state of the art. In addition, we carefully study the trade-off between cost savings obtained by continuously migrating tenants and the robustness of servers towards load spikes and failures.","PeriodicalId":87344,"journal":{"name":"Proceedings. ACM-SIGMOD International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91227980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jingkuan Song, Yang Yang, Yi Yang, Zi-Liang Huang, Heng Tao Shen
In this paper, we present a new multimedia retrieval paradigm to innovate large-scale search of heterogenous multimedia data. It is able to return results of different media types from heterogeneous data sources, e.g., using a query image to retrieve relevant text documents or images from different data sources. This utilizes the widely available data from different sources and caters for the current users' demand of receiving a result list simultaneously containing multiple types of data to obtain a comprehensive understanding of the query's results. To enable large-scale inter-media retrieval, we propose a novel inter-media hashing (IMH) model to explore the correlations among multiple media types from different data sources and tackle the scalability issue. To this end, multimedia data from heterogeneous data sources are transformed into a common Hamming space, in which fast search can be easily implemented by XOR and bit-count operations. Furthermore, we integrate a linear regression model to learn hashing functions so that the hash codes for new data points can be efficiently generated. Experiments conducted on real-world large-scale multimedia datasets demonstrate the superiority of our proposed method compared with state-of-the-art techniques.
{"title":"Inter-media hashing for large-scale retrieval from heterogeneous data sources","authors":"Jingkuan Song, Yang Yang, Yi Yang, Zi-Liang Huang, Heng Tao Shen","doi":"10.1145/2463676.2465274","DOIUrl":"https://doi.org/10.1145/2463676.2465274","url":null,"abstract":"In this paper, we present a new multimedia retrieval paradigm to innovate large-scale search of heterogenous multimedia data. It is able to return results of different media types from heterogeneous data sources, e.g., using a query image to retrieve relevant text documents or images from different data sources. This utilizes the widely available data from different sources and caters for the current users' demand of receiving a result list simultaneously containing multiple types of data to obtain a comprehensive understanding of the query's results. To enable large-scale inter-media retrieval, we propose a novel inter-media hashing (IMH) model to explore the correlations among multiple media types from different data sources and tackle the scalability issue. To this end, multimedia data from heterogeneous data sources are transformed into a common Hamming space, in which fast search can be easily implemented by XOR and bit-count operations. Furthermore, we integrate a linear regression model to learn hashing functions so that the hash codes for new data points can be efficiently generated. Experiments conducted on real-world large-scale multimedia datasets demonstrate the superiority of our proposed method compared with state-of-the-art techniques.","PeriodicalId":87344,"journal":{"name":"Proceedings. ACM-SIGMOD International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86489332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Stephan Ewen, Sebastian Schelter, K. Tzoumas, Daniel Warneke, V. Markl
Iterative algorithms occur in many domains of data analysis, such as machine learning or graph analysis. With increasing interest to run those algorithms on very large data sets, we see a need for new techniques to execute iterations in a massively parallel fashion. In prior work, we have shown how to extend and use a parallel data flow system to efficiently run iterative algorithms in a shared-nothing environment. Our approach supports the incremental processing nature of many of those algorithms. In this demonstration proposal we illustrate the process of implementing, compiling, optimizing, and executing iterative algorithms on Stratosphere using examples from graph analysis and machine learning. For the first step, we show the algorithm's code and a visualization of the produced data flow programs. The second step shows the optimizer's execution plan choices, while the last phase monitors the execution of the program, visualizing the state of the operators and additional metrics, such as per-iteration runtime and number of updates. To show that the data flow abstraction supports easy creation of custom programming APIs, we also present programs written against a lightweight Pregel API that is layered on top of our system with a small programming effort.
{"title":"Iterative parallel data processing with stratosphere: an inside look","authors":"Stephan Ewen, Sebastian Schelter, K. Tzoumas, Daniel Warneke, V. Markl","doi":"10.1145/2463676.2463693","DOIUrl":"https://doi.org/10.1145/2463676.2463693","url":null,"abstract":"Iterative algorithms occur in many domains of data analysis, such as machine learning or graph analysis. With increasing interest to run those algorithms on very large data sets, we see a need for new techniques to execute iterations in a massively parallel fashion. In prior work, we have shown how to extend and use a parallel data flow system to efficiently run iterative algorithms in a shared-nothing environment. Our approach supports the incremental processing nature of many of those algorithms.\u0000 In this demonstration proposal we illustrate the process of implementing, compiling, optimizing, and executing iterative algorithms on Stratosphere using examples from graph analysis and machine learning. For the first step, we show the algorithm's code and a visualization of the produced data flow programs. The second step shows the optimizer's execution plan choices, while the last phase monitors the execution of the program, visualizing the state of the operators and additional metrics, such as per-iteration runtime and number of updates.\u0000 To show that the data flow abstraction supports easy creation of custom programming APIs, we also present programs written against a lightweight Pregel API that is layered on top of our system with a small programming effort.","PeriodicalId":87344,"journal":{"name":"Proceedings. ACM-SIGMOD International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89301429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vivek R. Narasayya, Sudipto Das, M. Syamala, B. Chandramouli, S. Chaudhuri
Sharing resources of a single database server among multiple tenants is common in multi-tenant Database-as-a-Service providers, such as Microsoft SQL Azure. Multi-tenancy enables cost reduction for the cloud service provider which it can pass on as savings to the tenants. However, resource sharing can adversely affect a tenant's performance due to other tenants' workloads contending for shared resources. Service providers today do not provide any assurances to a tenant in terms of isolating its performance from other co-located tenants. SQLVM, a project at Microsoft Research, is an abstraction for performance isolation which is built on a promise of reserving key database server resources, such as CPU, I/O and memory, for each tenant. The key challenge is in supporting this abstraction within a RDBMS without statically allocating resources to tenants, while ensuring low overheads and scaling to large numbers of tenants. This demonstration will show how SQLVM can effectively isolate a tenant's performance from other tenant workloads co-located at the same database server. Our demonstration will use various scripted scenarios and a data collection and visualization framework to illustrate performance isolation using SQLVM.
{"title":"A demonstration of SQLVM: performance isolation in multi-tenant relational database-as-a-service","authors":"Vivek R. Narasayya, Sudipto Das, M. Syamala, B. Chandramouli, S. Chaudhuri","doi":"10.1145/2463676.2463686","DOIUrl":"https://doi.org/10.1145/2463676.2463686","url":null,"abstract":"Sharing resources of a single database server among multiple tenants is common in multi-tenant Database-as-a-Service providers, such as Microsoft SQL Azure. Multi-tenancy enables cost reduction for the cloud service provider which it can pass on as savings to the tenants. However, resource sharing can adversely affect a tenant's performance due to other tenants' workloads contending for shared resources. Service providers today do not provide any assurances to a tenant in terms of isolating its performance from other co-located tenants. SQLVM, a project at Microsoft Research, is an abstraction for performance isolation which is built on a promise of reserving key database server resources, such as CPU, I/O and memory, for each tenant. The key challenge is in supporting this abstraction within a RDBMS without statically allocating resources to tenants, while ensuring low overheads and scaling to large numbers of tenants. This demonstration will show how SQLVM can effectively isolate a tenant's performance from other tenant workloads co-located at the same database server. Our demonstration will use various scripted scenarios and a data collection and visualization framework to illustrate performance isolation using SQLVM.","PeriodicalId":87344,"journal":{"name":"Proceedings. ACM-SIGMOD International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89322704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abhishek Mukherji, Xika Lin, Christopher R. Botaish, Jason Whitehouse, Elke A. Rundensteiner, M. Ward, Carolina Ruiz
We demonstrate our PARAS technology for supporting interactive association mining at near real-time speeds. Key technical innovations of PARAS, in particular, stable region abstractions and rule redundancy management supporting novel parameter space-centric exploratory queries will be showcased. The audience will be able to interactively explore the parameter space view of rules. They will experience near real-time speeds achieved by PARAS for operations, such as comparing rule sets mined using different parameter values, that would otherwise take hours of computation and much manual investigation. Overall, we will demonstrate that the PARAS system provides a rich experience to data analysts through parameter tuning recommendations while significantly reducing the trial-and-error interactions.
{"title":"PARAS: interactive parameter space exploration for association rule mining","authors":"Abhishek Mukherji, Xika Lin, Christopher R. Botaish, Jason Whitehouse, Elke A. Rundensteiner, M. Ward, Carolina Ruiz","doi":"10.1145/2463676.2465245","DOIUrl":"https://doi.org/10.1145/2463676.2465245","url":null,"abstract":"We demonstrate our PARAS technology for supporting interactive association mining at near real-time speeds. Key technical innovations of PARAS, in particular, stable region abstractions and rule redundancy management supporting novel parameter space-centric exploratory queries will be showcased. The audience will be able to interactively explore the parameter space view of rules. They will experience near real-time speeds achieved by PARAS for operations, such as comparing rule sets mined using different parameter values, that would otherwise take hours of computation and much manual investigation. Overall, we will demonstrate that the PARAS system provides a rich experience to data analysts through parameter tuning recommendations while significantly reducing the trial-and-error interactions.","PeriodicalId":87344,"journal":{"name":"Proceedings. ACM-SIGMOD International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88163181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
At Facebook, we use various types of databases and storage system to satisfy the needs of different applications. The solutions built around these data store systems have a common set of requirements: they have to be highly scalable, maintenance costs should be low and they have to perform efficiently. We use a sharded mySQL+memcache solution to support real-time access of tens of petabytes of data and we use TAO to provide consistency of this web-scale database across geographical distances. We use Haystack data store for storing the 3 billion new photos we host every week. We use Apache Hadoop to mine intelligence from 100 petabytes of click logs and combine it with the power of Apache HBase to store all Facebook Messages. This paper describes the reasons why each of these databases is appropriate for that workload and the design decisions and tradeoffs that were made while implementing these solutions. We touch upon the consistency, availability and partitioning tolerance of each of these solutions. We touch upon the reasons why some of these systems need ACID semantics and other systems do not. We describe the techniques we have used to map the Facebook Graph Database into a set of relational tables. We speak of how we plan to do big-data deployments across geographical locations and our requirements for a new breed of pure-memory and pure-SSD based transactional database. Esteemed researchers in the Database Management community have benchmarked query latencies on Hive/Hadoop to be less performant than a traditional Parallel DBMS. We describe why these benchmarks are insufficient for Big Data deployments and why we continue to use Hadoop/Hive. We present an alternate set of benchmark techniques that measure capacity of a database, the value/byte in that database and the efficiency of inbuilt crowd-sourcing techniques to reduce administration costs of that database.
{"title":"Petabyte scale databases and storage systems at Facebook","authors":"Dhruba Borthakur","doi":"10.1145/2463676.2463713","DOIUrl":"https://doi.org/10.1145/2463676.2463713","url":null,"abstract":"At Facebook, we use various types of databases and storage system to satisfy the needs of different applications. The solutions built around these data store systems have a common set of requirements: they have to be highly scalable, maintenance costs should be low and they have to perform efficiently. We use a sharded mySQL+memcache solution to support real-time access of tens of petabytes of data and we use TAO to provide consistency of this web-scale database across geographical distances. We use Haystack data store for storing the 3 billion new photos we host every week. We use Apache Hadoop to mine intelligence from 100 petabytes of click logs and combine it with the power of Apache HBase to store all Facebook Messages.\u0000 This paper describes the reasons why each of these databases is appropriate for that workload and the design decisions and tradeoffs that were made while implementing these solutions. We touch upon the consistency, availability and partitioning tolerance of each of these solutions. We touch upon the reasons why some of these systems need ACID semantics and other systems do not. We describe the techniques we have used to map the Facebook Graph Database into a set of relational tables. We speak of how we plan to do big-data deployments across geographical locations and our requirements for a new breed of pure-memory and pure-SSD based transactional database.\u0000 Esteemed researchers in the Database Management community have benchmarked query latencies on Hive/Hadoop to be less performant than a traditional Parallel DBMS. We describe why these benchmarks are insufficient for Big Data deployments and why we continue to use Hadoop/Hive. We present an alternate set of benchmark techniques that measure capacity of a database, the value/byte in that database and the efficiency of inbuilt crowd-sourcing techniques to reduce administration costs of that database.","PeriodicalId":87344,"journal":{"name":"Proceedings. ACM-SIGMOD International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85351532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alekh Jindal, Jorge-Arnulfo Quiané-Ruiz, S. Madden
Modern enterprises have to deal with a variety of analytical queries over very large datasets. In this respect, Hadoop has gained much popularity since it scales to thousand of nodes and terabytes of data. However, Hadoop suffers from poor performance, especially in I/O performance. Several works have proposed alternate data storage for Hadoop in order to improve the query performance. However, many of these works end up making deep changes in Hadoop or HDFS. As a result, they are (i) difficult to adopt by several users, and (ii) not compatible with future Hadoop releases. In this paper, we present CARTILAGE, a comprehensive data storage framework built on top of HDFS. CARTILAGE allows users full control over their data storage, including data partitioning, data replication, data layouts, and data placement. Furthermore, CARTILAGE can be layered on top of an existing HDFS installation. This means that Hadoop, as well as other query engines, can readily make use of CARTILAGE. We describe several use-cases of CARTILAGE and propose to demonstrate the flexibility and efficiency of CARTILAGE through a set of novel scenarios.
{"title":"CARTILAGE: adding flexibility to the Hadoop skeleton","authors":"Alekh Jindal, Jorge-Arnulfo Quiané-Ruiz, S. Madden","doi":"10.1145/2463676.2465258","DOIUrl":"https://doi.org/10.1145/2463676.2465258","url":null,"abstract":"Modern enterprises have to deal with a variety of analytical queries over very large datasets. In this respect, Hadoop has gained much popularity since it scales to thousand of nodes and terabytes of data. However, Hadoop suffers from poor performance, especially in I/O performance. Several works have proposed alternate data storage for Hadoop in order to improve the query performance. However, many of these works end up making deep changes in Hadoop or HDFS. As a result, they are (i) difficult to adopt by several users, and (ii) not compatible with future Hadoop releases. In this paper, we present CARTILAGE, a comprehensive data storage framework built on top of HDFS. CARTILAGE allows users full control over their data storage, including data partitioning, data replication, data layouts, and data placement. Furthermore, CARTILAGE can be layered on top of an existing HDFS installation. This means that Hadoop, as well as other query engines, can readily make use of CARTILAGE. We describe several use-cases of CARTILAGE and propose to demonstrate the flexibility and efficiency of CARTILAGE through a set of novel scenarios.","PeriodicalId":87344,"journal":{"name":"Proceedings. ACM-SIGMOD International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79703805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aaron J. Elmore, Sudipto Das, A. Pucher, D. Agrawal, A. E. Abbadi, Xifeng Yan
A multitenant database management system (DBMS) in the cloud must continuously monitor the trade-off between efficient resource sharing among multiple application databases (tenants) and their performance. Considering the scale of attn{hundreds to} thousands of tenants in such multitenant DBMSs, manual approaches for continuous monitoring are not tenable. A self-managing controller of a multitenant DBMS faces several challenges. For instance, how to characterize a tenant given its variety of workloads, how to reduce the impact of tenant colocation, and how to detect and mitigate a performance crisis where one or more tenants' desired service level objective (SLO) is not achieved. We present Delphi, a self-managing system controller for a multitenant DBMS, and Pythia, a technique to learn behavior through observation and supervision using DBMS-agnostic database level performance measures. Pythia accurately learns tenant behavior even when multiple tenants share a database process, learns good and bad tenant consolidation plans (or packings), and maintains a pertenant history to detect behavior changes. Delphi detects performance crises, and leverages Pythia to suggests remedial actions using a hill-climbing search algorithm to identify a new tenant placement strategy to mitigate violating SLOs. Our evaluation using a variety of tenant types and workloads shows that Pythia can learn a tenant's behavior with more than 92% accuracy and learn the quality of packings with more than 86% accuracy. During a performance crisis, Delphi is able to reduce 99th percentile latencies by 80%, and can consolidate 45% more tenants than a greedy baseline, which balances tenant load without modeling tenant behavior.
{"title":"Characterizing tenant behavior for placement and crisis mitigation in multitenant DBMSs","authors":"Aaron J. Elmore, Sudipto Das, A. Pucher, D. Agrawal, A. E. Abbadi, Xifeng Yan","doi":"10.1145/2463676.2465308","DOIUrl":"https://doi.org/10.1145/2463676.2465308","url":null,"abstract":"A multitenant database management system (DBMS) in the cloud must continuously monitor the trade-off between efficient resource sharing among multiple application databases (tenants) and their performance. Considering the scale of attn{hundreds to} thousands of tenants in such multitenant DBMSs, manual approaches for continuous monitoring are not tenable. A self-managing controller of a multitenant DBMS faces several challenges. For instance, how to characterize a tenant given its variety of workloads, how to reduce the impact of tenant colocation, and how to detect and mitigate a performance crisis where one or more tenants' desired service level objective (SLO) is not achieved.\u0000 We present Delphi, a self-managing system controller for a multitenant DBMS, and Pythia, a technique to learn behavior through observation and supervision using DBMS-agnostic database level performance measures. Pythia accurately learns tenant behavior even when multiple tenants share a database process, learns good and bad tenant consolidation plans (or packings), and maintains a pertenant history to detect behavior changes. Delphi detects performance crises, and leverages Pythia to suggests remedial actions using a hill-climbing search algorithm to identify a new tenant placement strategy to mitigate violating SLOs. Our evaluation using a variety of tenant types and workloads shows that Pythia can learn a tenant's behavior with more than 92% accuracy and learn the quality of packings with more than 86% accuracy. During a performance crisis, Delphi is able to reduce 99th percentile latencies by 80%, and can consolidate 45% more tenants than a greedy baseline, which balances tenant load without modeling tenant behavior.","PeriodicalId":87344,"journal":{"name":"Proceedings. ACM-SIGMOD International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82587154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With current trends in integrating phylogenetic analysis into pharma-research, computing systems that integrate the two areas can help the drug discovery field. DrugTree is a tool that overlays ligand data on a protein-motivated phylogenetic tree. While initial tests of DrugTree are successful, it has been noticed that there are a number of lags concerning querying the tree. Due to the interleaving nature of the data, query optimization can become problematic since the data is being obtained from multiple sources, integrated and then presented to the user with the phylogenetic imposed upon the phylogenetic analysis layer. This poster presents our initial methodologies for addressing the query optimization issues. Our approach applies standards as well as uses novel mechanisms to help improve performance time.
{"title":"Mobile interaction and query optimizationin a protein-ligand data analysis system","authors":"Marvin Lapeine, K. Herbert, Emily Hill, N. Goodey","doi":"10.1145/2463676.2465344","DOIUrl":"https://doi.org/10.1145/2463676.2465344","url":null,"abstract":"With current trends in integrating phylogenetic analysis into pharma-research, computing systems that integrate the two areas can help the drug discovery field. DrugTree is a tool that overlays ligand data on a protein-motivated phylogenetic tree. While initial tests of DrugTree are successful, it has been noticed that there are a number of lags concerning querying the tree. Due to the interleaving nature of the data, query optimization can become problematic since the data is being obtained from multiple sources, integrated and then presented to the user with the phylogenetic imposed upon the phylogenetic analysis layer. This poster presents our initial methodologies for addressing the query optimization issues. Our approach applies standards as well as uses novel mechanisms to help improve performance time.","PeriodicalId":87344,"journal":{"name":"Proceedings. ACM-SIGMOD International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83601266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Martin Kaufmann, Amin Amiri Manjili, Panagiotis Vagenas, Peter M. Fischer, Donald Kossmann, Franz Färber, Norman May
Managing temporal data is becoming increasingly important for many applications. Several database systems already support the time dimension, but provide only few temporal operators, which also often exhibit poor performance characteristics. On the academic side, a large number of algorithms and data structures have been proposed, but they often address a subset of these temporal operators only. In this paper, we develop the Timeline Index as a novel, unified data structure that efficiently supports temporal operators such as temporal aggregation, time travel, and temporal joins. As the Timeline Index is independent of the physical order of the data, it provides flexibility in physical design; e.g., it supports any kind of compression scheme, which is crucial for main memory column stores. Our experiments show that the Timeline Index has predictable performance and beats state-of-the-art approaches significantly, sometimes by orders of magnitude.
{"title":"Timeline index: a unified data structure for processing queries on temporal data in SAP HANA","authors":"Martin Kaufmann, Amin Amiri Manjili, Panagiotis Vagenas, Peter M. Fischer, Donald Kossmann, Franz Färber, Norman May","doi":"10.1145/2463676.2465293","DOIUrl":"https://doi.org/10.1145/2463676.2465293","url":null,"abstract":"Managing temporal data is becoming increasingly important for many applications. Several database systems already support the time dimension, but provide only few temporal operators, which also often exhibit poor performance characteristics. On the academic side, a large number of algorithms and data structures have been proposed, but they often address a subset of these temporal operators only. In this paper, we develop the Timeline Index as a novel, unified data structure that efficiently supports temporal operators such as temporal aggregation, time travel, and temporal joins. As the Timeline Index is independent of the physical order of the data, it provides flexibility in physical design; e.g., it supports any kind of compression scheme, which is crucial for main memory column stores. Our experiments show that the Timeline Index has predictable performance and beats state-of-the-art approaches significantly, sometimes by orders of magnitude.","PeriodicalId":87344,"journal":{"name":"Proceedings. ACM-SIGMOD International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77917838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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