Data Warehousing Alternatives for Mobile Environments

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1 Data Warehousing Alternatives for Mobile Environments I. Stanoi D. Agrawal A. El Abbadi Department of Computer Science University of California Santa Barbara, CA S. H. Phatak B. R. Badrinath Department of Computer Science Rutgers University New Brunswick, NJ Abstract With rapid advancement in technology, mobile devices are increasingly becoming the norm. These devices are characterized by their need to operate even when they are disconnected from the fixed non-mobile world. Since existing software technology is tuned to applications that operate in a fully connected world, this requirement of disconnected operation creates a need to adapt existing software technology to a partially disconnected world. Databases and filesystems have already been adapted to operate in a partially disconnected environment. However, little work has been done in the context of data warehousing in such an environment. We believe that there is a real need for adapting existing data warehousing technology for the mobile world. In this position paper, we show how techniques for hierarchical data warehouse management can be applied to data warehouses in a mobile environment. The techniques can be extended for other mobile applications. We present a variety of alternatives for systems in which some of the sources of data as well as the data warehouse itself is mobile. 1 Introduction Traditional Data Warehousing models are restricted to merging information from and transmitting it to a set of connected components. This model involves a set of These authors were supported in part by NSF under grant numbers CCR and EIA These authors were supported in part by DARPA under contract numbers DAAH and DAAG , NSF under grant numbers CCR and IRIS , and Sponsors of WINLAB. base sources and a set of views that derive data from one or more base sources. Client applications run on the warehouse executing decision support queries, while the warehouse is connected to the data sources. One of the main problems in data warehouses is to maintain the consistency and currency of the materialized view in the presence of updates to the data sources [8, 1, 14, 15, 11, 5]. In mobile environments, a problem similar to view maintenance arises in the context of hosts that are disconnected from the networks [2, 3, 10, 9]. In particular, mobile hosts cache information from their home network (e.g., company intranet) and are disconnected for extended period of time. During disconnection the sources that contributed to the cached information might undergo updates rendering the mobile host cache or equivalently view obsolete. Thus, when mobile hosts connect to their home environment they need to undergo a process similar to the view maintenance in data warehousing. Current approaches handle this problem by simply discarding the cache and reloading the new information or view on the mobile host. For small sized cache this approach is reasonable. However, due to the advances in technology the size of mobile host view/cache can easily approach several hundreds of megabytes if not gigabytes. Using the naive approach for cache maintenance will incur significant overheads in terms of the networking. Due to these considerations, in this position paper we are exploring the feasibility of using incremental view maintenance techniques from data warehousing in the context of mobile applications. Aside from network overhead considerations, we believe that technological advances today are leading to the environment of a partially disconnected world. As we foresee it, Data Warehousing will also have to follow the same direction. The data stored in the materialized views of the data warehouse must also be accessible to mobile hosts: CEOs, COOs, CFOs need to carry data necessary for decision support with them and technological progress in terms of storage and computation power enables this scenario. At the other extreme (of

2 the CEOs/COOs/CFOs), point-of-sales, sales data are being generated on databases running on portable devices. These technological changes indicate that traditional data warehousing also needs to adapt to an environment where both the consumers of materialized views as well as producers of the data may be mobile platforms. Hence, incremental view maintenance techniques need to be developed for such environments. 2 System Models The basic model of a data warehouse is shown in Figure 1 [4]. The underlying model used for the data warehouse consists of n data sources, where data is generated and stored, and materialized views for storing and maintaining the information associated with the data warehouse. The information sources used for the computation of views can be diverse in nature ranging from well-structured information such as relational databases to semi-structured or unstructured information such as Internet web-pages or ASCII files. For example, the data model for the data sources and the views may be a relational data model, and the views could be defined as SPJ (Select-Project-Join) queries. In this paper, for simplicity, we will restrict all examples to the relational models and SPJ-views. Each data source is assumed to be completely autonomous in that the updates on different data sources are not related, and therefore not synchronized. However, we assume that for a given data source, updates are executed atomically. As shown in Figure 1, the updates at each source are monitored. As updates occur, they are transmitted asynchronously from the source to the data warehouse. All the updates performed atomically at a data source are sent as a single unit from the source to the data warehouse [15]. Based on this model of data warehouse, numerous algorithms have been proposed to maintain views incrementally [14, 15, 1]. Current data warehousing models assume that all sources and the data warehouse are are always connected. However, with advances in technology, mobile devices are increasingly becoming the norm. One key aspect of such systems is their ability to deal with disconnection. Disconnection refers to the condition when a mobile system is unable to communicate with some or all of its peers. In such a situation the mobile no longer has access to shared data. Furthermore, other nodes in the system can no longer access data on the mobile. Many commercial databases are being designed for such devices (e.g., Sybase s SQL Anywhere and Oracle 8i). The system model for such databases is shown in Figure 2. In a typical distributed database situation, any updates by node will have to be propagated to other peers. Disconnection is a failure mode in such a database system. However, in a mobile database, disconnection is an operational mode of the system. In this case, local commits are allowed while the mobile is disconnected and on reconnection the process of reintegration and reconciliation ensures global consistency. This mobile database model needs to be extended to data warehouses. Here, either the data warehouse can be mobile, or the sources can be mobile or both. In each of these cases, any updates need to be propagated to the data warehouse (which might be disconnected) and in turn the data warehouse needs to access one or more sources which might be disconnected. In order to deal with disconnections of mobiles from the home network, a proxy is often used to represent the mobile during the periods of disconnections. For example, if the mobile is participating in a query, the proxy can substitute for it during the disconnection of the mobile. Similarly, if the mobile is interested in gathering or integrating information from or to the network, it can assign this task to the proxy and disconnect from the network. 3 Data Warehousing in Mobile Environments We are mainly interested in the advantages we can obtain in mobile computing, by bringing forward concepts already existent in Data Warehousing. Previously, we have extended the basic model of Data Warehouses into a hierarchy of views, where views are able to derive data from base sources and/or other views [13]. This is a generalization of a warehouse with a single view or multiple independent views. The hierarchical structuring of the views results in several advantages that are especially appropriate for a disconnected environment. The decentralized assumption [13] on the hierarchical Data Warehouse also gives us more flexibility in a mobile environment. An important advantage is that the updates during disconnection can be incorporated in the views instead of waiting until the mobile connects to refresh its view. While there is no stringent requirement for Data Warehouses to use a minimum number of messages to compute effects of updates from multiple data sources, improvements are possible at the cost of a larger message sizes. The hierarchical structure reduces communication with base sources by providing precomputed partial updates to higher level views. Depending on the model, i.e., the amount of data that can be stored at the mobile hosts, we would like to optimize the communication with the outside world, while being able to derive all the necessary information. In the following, we consider different scenarios in which mobile systems interact with the data warehouse. We start with an environment in which only the hosts with materialized views are allowed to be mobile. We then relax this restriction and explore applications where data sources themselves are mobile.

3 Client Applications User Queries Data Warehouse Source 1 Source 2... Source n Figure 1: A System Model for a Data Warehouse mobile client responses requests local server local replica hoard requests data data for reintegration server requests responses normal client Mobile Host Figure 2: A System Model for Mobile Databases 3.1 Mobility of Views Webeginwithanarchitectureinwhichtheviewhierarchy corresponding to the data warehouse is stored at a proxy. The mobile hosts connect to the data warehouse and refresh their views from their counterparts on the proxy as shown in Figure 3. The data warehouse as shown here, is on a proxy server, which need not be a single machine, and hence algorithms developed for data warehouses in [12] can be used to maintain the view on the proxy from the distributed data sources. Each time the mobile reconnects, it can either re-replicate the entire view or use the difference between its replica and the proxy to update its local view. Note that the difference need not be explicitly computed, rather timestamps can be associated with each tuple in the view, and the difference reduces to a select operation that selects tuples introduced into the view after the mobile replica was created. The hierarchical arrangement of caches on a proxy is a general case of the single or multiple independent cache model. Mobile hosts connect to the proxy, and by accessing the corresponding view, can easily download the set of updates that were incorporated since the last connection. Updates are precomputed and stored at the views, in a table of changes. The table of changes is a set of tuples along with the dependency information which identifies the source associated with that change. This additional information is used to subtract update effects as requested by any of the view s descendents. It is shown in [13] that, since the order of updates may not be the same as that requested by descendents trying to update their views, a view answering queries needs to store sufficient information in the s to locally exclude the effects of unwanted outof-order updates. Note that the idea of maintaining and using information from the table of changes eliminates the need for multiple versions of a view s data [11, 6, 7] This model offers some key advantages, as compared to a system where mobile units connect directly to the

4 proxy V4 V3 V1 V2 S1 S2 S3 S4 Figure 3: Mobile Hosts that Access Views base sources: 1. reduced replication of information, since mobile hosts that access the same information can use the same predefined view. 2. the proxy needs to interact with the external base sources only when updating the views directly dependent on base sources. 3. updates are precomputed and accessible to mobile hosts on connection. We assume that mobile hosts are interested in read-only views. However, if it is required that mobile hosts be allowed update locally stored information, such hosts only need to log the updates and send them through the proxy to the initial base sources. This way concurrency control issues are solved outside of the proxy, at the location of the base sources, and other mobile hosts interested in dependent information will eventually have access to the same set of updates. Updating views integrate the changes after they are received from the proxy, rather than integrating them when the decision to update is made. We now extend the architecture to eliminate the need to store the views explicitly at the proxy as shown in Figure 4. Our goal is to obtain the same important advantage as before, that of providing precomputed updates to mobile hosts. At the same time, we want to replicate data on the proxy in a minimal way. If we do not store entire views on the proxy, we reduce the storage requirements by creating a hierarchy of tables of changes (s) rather than the the previously described hierarchy of views. In this set-up, the incremental maintenance of the table of changes is relatively complex since the algorithm needs to access the state of materialized views to compute the changes. For example, in Figure 4, in order to compute the changes in 3 due to an update in the data source S 2, the following query must be computed: 3 = 1 1 ( V 2 )+( 1 + V 1 ) 1 2 That is, during the computation of the new set of updates at 3, we need information about not only data stored at 1 and 2, but also the associated views V 1 and V 2. There is an obvious trade-off between the advantage of giving mobile hosts access to precomputed updates, and the possibility of a significant delay in calculating these updates. If mobile hosts (views) connect frequently, then the proxy can send the corresponding partial subquery based on s, and mobile hosts answer according to the state of their local data. If mobile hosts store both the materialized view and the table of changes, then subtraction of unnecessary updates can be done at the proxy, and the computation of subquery results at the mobile hosts can be based solely on the materialized views (excluding the ). If the frequency of connection to the proxy is low, then the advantage obtained by deriving views directly from other views is overshadowed by the disadvantage of unnecessary delays in computing updates. If s are required to store a deriving schema as a function of other views as well as a schema based only on base sources, then the proxy can bypass the slow connections and derive the necessary data directly from base sources. A combination of the two solutions requires statistical knowledge of frequency of connection, and a careful selection of duplicate updates which should be discarded. In the worst case, in order to avoid communication with base sources for each update of each view, a copy of the base relations can be also stored at the proxy.

5 proxy 4 V4 V3 3 V1 1 2 V2 S1 S2 S3 S4 Figure 4: Mobile Hosts that Store Views 3.2 Mobility Everywhere We now eliminate the need for a stationary proxy that acted as an intermediary between the data sources and the mobile hosts. This architecture is shown in Figure 5. Hence, mobile hosts are themselves responsible, on connection, for the correct computation of updates to their respective materialized views. The data warehouse is placed directly on the mobile system. In this case the mobile either queries other mobile hosts, or it directly communicates with the data sources to create and maintain the view. The main difficulty in this architecture is that update reports will not be received by the client when it is disconnected. An alternative is to replicate each of the base relations on the mobile and have the sources send their update logs on each reconnection. Unfortunately, this solution might need prohibitive amounts of disk space. V1 V3 S1 S2 S3 S4 Figure 5: s and Views Stored on Mobile Hosts V2 V4 The basic idea of incremental view maintenance requires that if there is a set of updates S i for each source S i, then a query needs to be formulated to compute the effect of these updates on the materialized view V which is defined as S S i S n. The view change query is as follows: V = (1) S1 1 (S 2 + S 2) 1...(S n + S n) (2) + S 1 1 S (S n + S n) (3) S S i (S n S n) (4) + S S n 1 1 S n (5) The n subqueries can be processed concurrently, by dispatching only two queries to each of the ancestors. Since reducing the interaction between sites that are possibly disconnected is crucial in a mobile environment, it may be efficient to divide an update phase into only two steps. First, messages are forwarded sequentially to site S 1,thenS 2 S n in increasing order of their indices. A site S i receives a set of partially computed subqueries, calculates their effects on local data (S i + S i ) and forwards the results together with Si to S i+1. Note that at the end of the first step, only one of the subqueries (Equation 1) is complete. A subsequent phase is therefore needed to complete the rest of the subqueries. A view S i forwards all the results of the subqueries to S i 1, with the exception of subqueries which started at S i or later during the first phase. These subqueries are joined with the data at S i with the exception of the new set of updates S i. At the completion of a subquery result, the answer is sent to the updating

6 view V. Consider a restricted environment where a single data source, S 1, is mobile. Then both phases can be completed without additional delay imposed by the disconnecting source. However, as more base sources become mobile, the delay involved in the computation and propagation of updates increases. We are currently investigating more efficient protocols for view maintenance in the general case of mobility everywhere. 4 Discussion Introducing mobile nodes in a data warehousing system poses unique challenges. Since such nodes are likely to be disconnected for large periods of time, the underlying algorithms for maintaining the data warehouse can no longer assume that the mobile will deliver updates as they occur, or that the data on the mobile can be queried. Similarly, if a mobile stores a data warehouse then updates from the sources will not be sent to the mobile until it reconnects. We have proposed several models for considering solutions to the problem of view maintenance with disconnections. We hope that the solutions sketched in this paper will produce further discussion and research on view maintenance in mobile environments. The problem of handling disconnection has been addressed in the context of cache consistency [2], transaction models [3, 10], reconciliation and reintegration [9]. For the case where the data warehouse (view) is mobile, [6, 7] provide a proxy based solution with multiversioning. In this paper, on the other hand, we discuss a variety of architectures for mobile views, from proxy based to non-proxy based systems. We also introduce the concept of a mobile source. We have sketched architectural alternatives ranging from the use of proxies for mobile systems to architectures where every source and/or the data warehouse can be mobile. Each solution has its unique problems and key research issues. To our knowledge this is the first attempt towards providing a comprehensive solution for the problem of handling disconnection in hierarchical data warehouses. References [1] D.Agrawal,A.ElAbbadi,A.Singh,andT.Yurek. Efficient View Maintenance in Data Warehouses. In Proceedings of the 1997 ACM International Conference on Management of Data, pages , May [2] Daniel Barbara and Tomasz Imilienski. Sleepers and Workaholics: Caching Strategies in Mobile Environments. In Proceedings of the ACM SIGMOD International Conference on Management of Data, [3] Panos Chrysanthis. Transaction Processing in Mobile Computing Environment. IEEE Workshop on Advances in Parallel and Distributed Systems, October [4] J. Hammer, H. Garcia-Molina, J. Widom, W. Labio, and Y. Zhuge. The Stanford Data Warehousing Project. IEEE Bulletin of the Technical Committee on Data Engineering, 18(2):41 48, June [5] N. Huyn. Multiple-View Self-Maintenance in Data Warehousing Environments. In Proceedings of the International Conference on Very Large Data Bases, [6] S. Weissman Lauzac and P. K. Chrysanthis. Programming Views for Mobile Database Clients. In Proceedings of the 9th International Workshop on Database and Expert Systems and Applications, August [7] S. Weissman Lauzac and P. K. Chrysanthis. Utilizing Versions of Views within a Mobile Environment. In Proceedings of the 9th International Conference on Computing and Information, June [8]I.S.Mumick,D.Quass,andB.S.Mumick.Maintenance of Data Cubes and Summary Tables in a Warehouse. In Proceedings of the ACM SIGMOD International Conference on Management of Data, May [9] Shirish Phatak and B. R. Badrinath. Multiversion Reconciliation for Mobile Databases. In Proceedings of the 15th International Conference on Data Engineering, [10] Evaggelia Pitoura and Bharat Bhargava. Building Information Systems for Mobile Environments. In Proceedings of the CIKM conference, [11] D. Quass and J. Widom. On-line Warehouse View Maintenance. In Proceedings of the ACM SIGMOD International Conference on Management of Data, May [12] I. Stanoi, D. Agrawal, and A. El Abbadi. Weak Consistency in Distributed Data Warehouses. In Proceedings of the International Conference of Foundations of Data Organization, November [13] I. Stanoi, D. Agrawal, and A. El Abbadi. Modeling and maintaining multi-view data warehouses. Technical report, Department of Computer Science, University of California at Santa Barbara,TRCS99-21, June 1999.

7 [14] Yue Zhuge, Hector Garcia-Molina, Joachim Hammer, and Jennifer Widom. View Maintenance in a Warehousing Environment. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages , May [15] Yue Zhuge, Hector Garcia-Molina, and Janet L. Wiener. The Strobe Algorithms for Multi-Source Warehouse Consistency. In Proceedings of the International Conference on Parallel and Distributed Information Systems, December 1996.

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