An Overview of Business Intelligence Technology | August 2011

By Surajit Chaudhuri, ­meshwar Dayal, Vivek Narasayya Communications of the ACM, August 2011, Vol. 54

flowchart

Business intelligence (BI) software is a collection of
decision support technologies for the enterprise aimed at
enabling knowledge workers such as executives, managers, and
analysts to make better and faster decisions. The past two
decades have seen explosive growth, both in the number of
products and services offered and in the adoption of these
technologies by industry. This growth has been fueled by the
declining cost of acquiring and storing very large amounts of
data arising from sources such as customer transactions in
banking, retail as well as in e-businesses, RFID tags for
inventory tracking, email, query logs for Web sites, blogs, and
product reviews. Enterprises today collect data at a finer
granularity, which is therefore of much larger volume. Businesses
are leveraging their data asset aggressively by deploying and
experimenting with more sophisticated data analysis techniques to
drive business decisions and deliver new functionality such as
personalized offers and services to customers. Today, it is
difficult to find a successful enterprise that has not leveraged
BI technology for its business. For example, BI technology is
used in manufacturing for order shipment and customer support, in
retail for user profiling to target grocery coupons during
checkout, in financial services for claims analysis and fraud
detection, in transportation for fleet management, in
telecommunications for identifying reasons for customer churn, in
utilities for power usage analysis, and health care for outcomes
analysis.

Back to Top

Key Insights

ins01.gif

A typical architecture for supporting BI within an enterprise
is shown in Figure 1 (the shaded boxes are
technology that we focus on in this article). The data over which
BI tasks are performed often comes from different
sources—typically from multiple operational databases
across departments within the organization, as well as external
vendors. Different sources contain data of varying quality, use
inconsistent representations, codes, and formats, which have to
be reconciled. Thus the problems of integrating, cleansing, and
standardizing data in preparation for BI tasks can be rather
challenging. Efficient data loading is imperative for BI.
Moreover, BI tasks usually need to be performed incrementally as
new data arrives, for example, last month’s sales data. This
makes efficient and scalable data loading and refresh
capabilities imperative for enterprise BI. These back-end
technologies for preparing the data for BI are collectively
referred to as Extract-Transform-Load (ETL) tools. Increasingly
there is a need to support BI tasks in near real time, that is,
make business decisions based on the operational data itself.
Specialized engines referred to as Complex Event Processing (CEP)
engines have emerged to support such scenarios.

The data over which BI tasks are performed is typically loaded
into a repository called the data warehouse that is
managed by one or more data warehouse servers. A popular choice
of engines for storing and querying warehouse data is relational
database management systems (RDBMS). Over the past two decades,
several data structures, optimizations, and query processing
techniques have been developed primarily for executing complex
SQL queries over large volumes of data—a key requirement
for BI. An example of such an ad hoc SQL query is: find customers
who have placed an order during the past quarter whose amount
exceeds the average order amount by at least 50%. Large data
warehouses typically deploy parallel RDBMS engines so that SQL
queries can be executed over large volumes of data with low
latency.

As more data is born digital, there is increasing desire to
architect low-cost data platforms that can support much larger
data volume than that traditionally handled by RDBMSs. This is
often described as the “Big Data” challenge. Driven by this goal,
engines based on the MapReduce9
paradigm—originally built for analyzing Web documents and
Web search query logs—are now being targeted for enterprise
analytics. Such engines are currently being extended to support
complex SQL-like queries essential for traditional enterprise
data warehousing scenarios.

Data warehouse servers are complemented by a set of
mid-tier servers that provide specialized functionality
for different BI scenarios. Online analytic processing (OLAP)
servers efficiently expose the multidimensional view of
data to applications or users and enable the common BI operations
such as filtering, aggregation, drill-down and pivoting. In
addition to traditional OLAP servers, newer “in-memory BI”
engines are appearing that exploit today’s large main memory
sizes to dramatically improve performance of multidimensional
queries. Reporting servers enable definition, efficient
execution and rendering of reports—for example, report
total sales by region for this year and compare with sales from
last year. The increasing availability and importance of text
data such as product reviews, email, and call center transcripts
for BI brings new challenges. Enterprise search engines
support the keyword search paradigm over text and structured data
in the warehouse (for example, find email messages, documents,
history of purchases and support calls related to a particular
customer), and have become a valuable tool for BI over the past
decade. Data mining engines enable in-depth analysis of
data that goes well beyond what is offered by OLAP or reporting
servers, and provides the ability to build predictive models to
help answer questions such as: which existing customers are
likely to respond to my upcoming catalog mailing campaign?
Text analytic engines can analyze large amounts of text
data (for example, survey responses or comments from customers)
and extract valuable information that would otherwise require
significant manual effort, for example, which products are
mentioned in the survey responses and the topics that are
frequently discussed in connection with those products.

There are several popular front-end applications through which
users perform BI tasks: spreadsheets, enterprise portals for
searching, performance management applications that enable
decision makers to track key performance indicators of the
business using visual dashboards, tools that allow users to pose
ad hoc queries, viewers for data mining models, and so on. Rapid,
ad hoc visualization of data can enable dynamic
exploration of patterns, outliers and help uncover relevant facts
for BI.

In addition, there are other BI technologies (not shown in
Figure 1) such as Web analytics, which
enables understanding of how visitors to a company’s Web site
interact with the pages; for example which landing pages are
likely to encourage the visitor to make a purchase. Likewise,
vertical packaged applications such as customer relationship
management (CRM) are widely used. These applications often
support built-in analytics, for example, a CRM application might
provide functionality to segment customers into those most likely
and least likely to repurchase a particular product. Another
nascent but important area is mobile BI that presents
opportunities for enabling novel and rich BI applications for
knowledge workers on mobile devices.

In this short article, we are not able to provide
comprehensive coverage of all technologies used in BI (see
Chaudhuri et al.5 for additional
details on some of these technologies). We therefore chose to
focus on technology where research can play, or has historically
played, an important role. In some instances, these technologies
are mature but challenging research problems still
remain—for example, data storage, OLAP servers, RDBMSs, and
ETL tools. In other instances, the technology is relatively new
with several open research challenges, for example, MapReduce
engines, near real-time BI, enterprise search, data mining and
text analytics, cloud data services.

Back to Top

Data Storage

Access structures. Decision support queries require
operations such as filtering, join, and aggregation. To
efficiently support these operations, special data structures
(not typically required for OLTP queries) have been developed in
RDBMSs, described here. Access structures used in specialized
OLAP engines that do not use RDBMSs are discussed later.

Index structures. An index enables associative access
based on values of a particular column. When a query has one or
more filter conditions, the selectivities of these conditions can
be exploited through index scans (for example, an index on
the StoreId column can help retrieve all sales for StoreId = 23)
and index intersection (when multiple conditions exist).
These operations can significantly reduce, and in some cases
eliminate, the need to access the base tables, for example, when
the index itself contains all columns required to answer the
query. Bitmap indexes support efficient index operations
such as union and intersection. A bitmap index on a column uses
one bit per record for each value in the domain of that column.
To process a query of the form column1 = val1 AND
column2 = val2 using bitmap indexes, we identify the
qualifying records by taking the bitwise AND of the respective
bit vectors. While such representations are very effective for
low cardinality domains (for example, gender), they can also be
used for higher cardinality domains using bitmap compression.


Today, it is difficult to find a successful
enterprise that has not leveraged BI technology for their
business.


Materialized views. Reporting queries often require
summary data, for example, aggregate sales of the most recent
quarter and the current fiscal year. Hence, precomputing and
materializing summary data (also referred to as materialized
views) can help dramatically accelerate many decision support
queries. The greatest strength of a materialized view is its
ability to specifically target certain queries by effectively
caching their results. However this very strength also can limit
its applicability, that is, for a slightly different query it may
not be possible to use the materialized view to answer that
query. This is in contrast to an index, which is a much more
general structure, but whose impact on query performance may not
be as dramatic as a materialized view. Typically, a good physical
design contains a judicious mix of indexes and materialized
views.

Partitioning. Data partitioning can be used to improve
both performance (discussed later) and manageability.
Partitioning allows tables and indexes to be divided into
smaller, more manageable units. Database maintenance operations
such as loading and backup can be performed on partitions rather
than an entire table or index. The common types of partitioning
supported today are hash and range. Hybrid schemes that first
partition by range followed by hash partitioning within each
range partition are also common.

Column-oriented storage. Traditional relational
commercial database engines store data in a row-oriented manner,
that is, the values of all columns for a given row in a table are
stored contiguously. The Sybase IQ
product30 pioneered the use of
column-oriented storage, where all values of a particular
column are stored contiguously. This approach optimizes
for “read-mostly” workloads of ad hoc queries. The
column-oriented representation has two advantages. First,
significantly greater data compression is possible than in a
row-oriented store since data values within a column are
typically much more repetitive than across columns. Second, only
the columns accessed in the query need to be scanned. In
contrast, in a row-oriented store, it is not easy to skip columns
that are not accessed in the query. Together, this can result in
reduced time for scanning large tables.

Finally, we note that in the past decade, major commercial
database systems have added automated physical design tools that
can assist database administrators (DBAs) in choosing appropriate
access structures (see Chaudhuri and
Narasayya7 for an overview) based on
workload information, such as queries and updates executed
on the system, and constraints, for example, total storage
allotted to access structures.

Data Compression can have significant benefits for
large data warehouses. Compression can reduce the amount of data
that needs to be scanned, and hence the I/O cost of the query.
Second, since compression reduces the amount of storage required
for a database, it can also lower storage and backup costs. A
third benefit is that compression effectively increases the
amount of data that can be cached in memory since the pages can
be kept in compressed form, and decompressed only on demand.
Fourth, certain common query operations (for example, equality
conditions, duplicate elimination) can often be performed on the
compressed data itself without having to decompress the data.
Finally, compressing data that is transferred over the network
effectively increases the available network bandwidth. This is
important for parallel DBMSs where data must be moved across
nodes. Data compression plays a key role not just in relational
DBMSs, but also in other specialized engines, for example, in
OLAP.

There are different compression techniques used in relational
DBMSs. Null suppression leverages the fact that several
commonly used data types in DBMSs are fixed length (for
example, int, bigint, datetime, money), and significant
compression is possible if they are treated as variable length
for storage purposes. Only the non-null part of the value is
stored along with the actual length of the value. Dictionary
compression
identifies repetitive values in the data and
constructs a dictionary that maps such values to more compact
representations. For example, a column that stores the shipping
mode for an order may contain string values such as ‘AIR’,
‘SHIP’, ‘TRUCK’. Each value can be represented using two bits by
mapping them to values 0,1,2 respectively. Finally, unlike
compression schemes in row-oriented stores where each instance of
a value requires an entry (potentially with fewer bits), in
column-oriented stores other compression techniques such as
run-length encoding (RLE) can become more effective. In
RLE compression, a sequence of k instances of value
v is encoded by the pair (v,k). RLE is particularly
attractive when long runs of the same value occur; this can
happen for columns with relatively few distinct values, or when
the column values are sorted.

There are several interesting technical challenges in data
compression. First, new compression techniques suitable for large
data warehouses and incurring an acceptable trade-off with
decompression and update costs are important. Second, even for
known compression techniques important open problems
remain—for example, for RLE—the choice of sort order
of the table can significantly affect the amount of compression
possible. Determining the best sort order to use is a non-trivial
optimization problem. Finally, the decision of whether to
compress access structures is workload dependent. Thus, there is
a need for automated physical design tools to also recommend
which access structures should be compressed and how based on
workload information.

Back to Top

Query Processing

A popular conceptual model used for BI tasks is the
multidimensional view of data, as shown in
Figure
2
. In a multidimensional data model, there is a set of
numeric measures that are the objects of analysis.
Examples of such measures are sales, budget, revenue, and
inventory. Each of the numeric measures is associated with a set
of dimensions, which provide the context for the measure.
For example, the dimensions associated with a sale amount can be
the Product, City, and the Date when the sale was made. Thus, a
measure can be viewed as a value in the multidimensional space of
dimensions. Each dimension is described by a set of attributes,
for example, the Product dimension may consist of the following
attributes: the category, industry, model number, year of its
introduction. The attributes of a dimension may be related via a
hierarchy of relationships. For example, a product is
related to its category and the industry attributes through a
hierarchical relationship (Figure 2). Another
distinctive feature of the conceptual model is its stress on
aggregation of measures by one or more dimensions; for
example, computing and ranking the total sales by each
county for each year.

OLAP Servers. Online Analytic processing (OLAP)
supports operations such as filtering, aggregation, pivoting,
rollup and drill-down on the multi-dimensional view of the data.
OLAP servers are implemented using either a multidimensional
storage engine (MOLAP); a relational DBMS engine (ROLAP) as the
backend; or a hybrid combination called HOLAP.

MOLAP servers. MOLAP servers directly support the
multidimensional view of data through a storage engine that uses
the multidimensional array abstraction. They typically precompute
large data cubes to speed up query processing. Such an
approach has the advantage of excellent indexing properties and
fast query response times, but provides relatively poor storage
utilization, especially when the data set is sparse. To better
adapt to sparse data sets, MOLAP servers identify dense and
sparse regions of the data, and store/index these regions
differently. For example dense sub-arrays of the cube are
identified and stored in array format, whereas the sparse regions
are compressed and stored separately.

ROLAP servers. In ROLAP, the multidimensional model and
its operations have to be mapped into relations and SQL queries.
They rely on the data storage techniques described earlier to
speed up relational query processing. ROLAP servers may also need
to implement functionality not supported in SQL, for example,
extended aggregate functions such as median, mode, and
time window based moving average. The database designs
used in ROLAP are optimized for efficiency in querying and in
loading data. Most ROLAP systems use a star schema to
represent the multidimensional data model. The database consists
of a single fact table and a single table for each dimension.
Each row in the fact table consists of a pointer (a.k.a. foreign
key) to each of the dimensions that provide its multidimensional
coordinates, and stores the numeric measures for those
coordinates. Each dimension table consists of columns that
correspond to attributes of the dimension. Star schemas do not
explicitly provide support for attribute hierarchies.
Snowflake schemas (shown in Figure 3)
provide a refinement of star schemas where the dimensional
hierarchy is explicitly represented by normalizing the dimension
tables. This leads to advantages in maintaining the dimension
tables.

HOLAP servers. The HOLAP architecture combines ROLAP
and MOLAP by splitting storage of data in a MOLAP and a
relational store. Splitting the data can be done in different
ways. One method is to store the detailed data in a RDBMS as
ROLAP servers do, and precomputing aggregated data in MOLAP.
Another method is to store more recent data in MOLAP to provide
faster access, and older data in ROLAP. Since MOLAP performs
better when the data is reasonably dense and ROLAP servers
perform for sparse data, Like MOLAP servers, HOLAP servers also
perform density analysis to identify sparse and dense sub-regions
of the multidimensional space. All major data warehouse vendors
today offer OLAP servers (for example, IBM
Cognos,15 Microsoft
SQL,17 and Oracle
Hyperion23).

In-memory BI engines. Technology trends are providing
an opportunity for a new class of OLAP engines focused on
exploiting large main memory to make response times for ad-hoc
queries
interactive. First, the ratio of time to access data
on disk vs. data in memory is increasing. Second, with 64-bit
operating systems becoming common, very large addressable memory
sizes (for example, 1TB) are possible. Third, the cost of memory
has dropped significantly, which makes servers with large amounts
of main memory affordable. Unlike traditional OLAP servers,
in-memory BI engines (for example,
QlikView24) rely on a different set
of techniques for achieving good performance. First, since the
detailed data is memory resident they avoid expensive I/Os
required to access data cubes, indexes, or materialized views.
Second, they use data structures that would not be suitable for
disk-based access, but are very effective for in-memory access.
For example, consider a query that computes the total sales for
each customer in a particular state. When the data is initially
loaded into the system, the engine can associate pointers
from each state to customers in that state, and similarly
pointers from a customer to all the order detail records for that
customer. This allows fast associative access required to answer
the query quickly, and is reminiscent of approaches used by
object-oriented databases as well as optimizations in traditional
DBMSs such as join indices. Third, in-memory BI engines
can significantly increase the effective data sizes over which
they can operate in memory by using data organization techniques
such as column-oriented storage and data compression. In-memory
BI engines are best suited for read-mostly data without in-place
data updates where new data arrives primarily in the form of
incremental batch inserts due to data decompression cost.

Relational Servers. Relational database servers
(RDBMSs) have traditionally served as the backend of large data
warehouses. Such data warehouses need to be able to execute
complex SQL queries as efficiently as possible against
very large databases. The first key technology needed to
achieve this is query optimization, which takes a complex query
and compiles that query into an execution plan. To ensure that
the execution plan can scale well to large databases, data
partitioning and parallel query processing are leveraged
extensively (see Graefe13 for an
overview of query processing techniques). We therefore discuss
two pieces of key technology—query optimization and
parallel query processing.

Query optimization technology has been a key enabler
for BI. The query optimizer is responsible for selecting an
execution plan for answering a query. The execution plan
is a composition of physical operators (such as Index Scan, Hash
Join, Sort) that when evaluated generates the results of the
query. The performance of a query crucially depends on the
ability of the optimizer to choose a good plan from a very large
space of alternatives. The difference in execution time between a
good and bad plan for such complex queries can be several orders
of magnitudes (for example, days instead of minutes). This topic
has been of keen interest in database research and industry (an
overview of the field appears in
Chaudhuri4). Following the pioneering
work done in the System R optimizer from IBM Research in the late
1970s, the next major architectural innovation came about a
decade later: extensible optimizers. This allowed system
designers to “plug-in” new rules and extend the
capabilities of the optimizer. For example, a rule could
represent equivalence in relational algebra (for example, pushing
down an aggregation below join). Application of such rules can
potentially transform the execution plan into one that executes
much faster. Extensible optimizers allowed many important
optimizations developed by industry and research over the years
to be incorporated relatively easily without having to repeatedly
modify the search strategy of the optimizer.

Despite the success of query optimization and the crucial role
it plays in BI, many fundamental challenges still remain. The
optimizer needs to address the inherently difficult problem of
estimating the cost of a plan, that is, the total work
(CPU, I/O, among others) done by the plan. However, constrained
by the requirement to impose only a small overhead, the optimizer
typically uses limited statistical information such as histograms
describing a column’s data distribution. Such approximations
sometimes result in brittleness since large inaccuracies can lead
to generation of very poor plans. There has been research in
leveraging feedback from query execution to overcome errors made
by the query optimizer by observing actual query execution
behavior (for example, the actual result size of a query
expression), and adjusting the execution plan if needed. However,
collecting and exploiting feedback at low overhead is also
challenging, and much more work is needed to realize the benefits
of this approach.

Parallel processing and appliances. Parallelism plays a
significant role in processing queries over massive databases.
Relational operators such as selection, projection, join, and
aggregation present many opportunities for parallelism. The basic
paradigm is data parallelism, that is, to apply relational
operators in parallel on disjoint subsets of data (partitions),
and then combine the results. The article by Dewitt and
Gray10 provides an overview of work
in this area. For several years now, all major vendors of
database management systems have offered data partitioning and
parallel query processing technology. There are two basic
architectures for parallelism: Shared disk, where each
processor has a private memory but shares disks with all other
processors. Shared nothing, where each processor has
private memory and disk and is typically a low-cost commodity
machine. Interestingly, while these architectures date back about
two decades, neither has yet emerged as a clear winner in the
industry and successful implementations of both exist today.

In shared disk systems all nodes have access to the data via
shared storage, so there is no need to a priori partition the
data across nodes as in the shared nothing approach. During query
processing, there is no need to move data across nodes. Moreover,
load balancing is relatively simple since any node can service
any request. However, there are a couple of issues that can
affect scalability of shared disk systems. First, the nodes need
to communicate in order to ensure data consistency. Typically
this is implemented via a distributed lock manager, which
can incur non-trivial overhead. Second, the network must support
the combined I/O bandwidth of all processors, and can become a
bottleneck. Shared disk systems are relatively cost effective for
small- to medium-sized data warehouses.

In shared nothing systems (for example,
Teradata31) data needs to be
distributed across nodes a priori. They have the potential to
scale to much larger data sizes than shared disk systems.
However, the decision of how to effectively distribute the data
across nodes is crucial for performance and scalability. This is
important both from the standpoint of leveraging parallelism, but
also to reduce the amount of data that needs to be transferred
over the network during query processing. Two key techniques for
data distribution are partitioning and cloning. For example
consider a large database with the schema shown in
Figure 3. Each of the two large fact tables,
Orders and OrderDetails can be hash partitioned
across all nodes on the OrderId attribute respectively,
that is, on the attribute on which the two tables are joined. All
other dimension tables, which are relatively small, could be
cloned (replicated) on each node. Now consider a query that joins
Customers, Orders and OrderDetails. This query can
be processed by issuing one query per node, each operating on a
subset of the fact data and joining with the entire dimension
table. As a final step, the results of each of these queries are
sent over the network to a single node that combines them to
produce the final answer to the query.

Data warehouse appliances. Recently a new generation of
parallel DBMSs referred to as data warehouse appliances (for
example, Netezza19) have appeared. An
appliance is an integrated set of server and storage hardware,
operating system and DBMS software specifically
pre-installed and pre-optimized for data
warehousing. These appliances have gained impetus from the
following trends. First, since DW appliance vendors control the
full hardware/software stack, they can offer the more attractive
one service call model. Second, some appliances push part of the
query processing into specialized hardware thereby speeding up
queries. For example, Netezza uses FPGAs (field-programmable gate
arrays) to evaluate selection and projection operators on a table
in the storage layer itself. For typical decision support queries
this can significantly reduce the amount of data that needs to be
processed in the DBMS layer.

Distributed Systems using Map-Reduce Paradigm.
Large-scale data processing engines based on the
Map-Reduce paradigm9 were
originally developed to analyze Web documents, query logs, and
click-through information for index generation and for improving
Web search quality. Platforms based on a distributed file system
and using the MapReduce runtime (or its variants such as
Dryad16) have been successfully
deployed on clusters with an order of magnitude more nodes than
traditional parallel DBMSs. Also, unlike parallel DBMSs where the
data must first be loaded into a table with a predefined schema
before it can be queried, a MapReduce job can directly be
executed on schema-less input files. Furthermore, these data
platforms are able to automatically handle important issues such
as data partitioning, node failures, managing the flow of data
across nodes, and heterogeneity of nodes.

Data platforms based on the MapReduce paradigm and its
variants have attracted strong interest in the context of the
“Big Data” challenge in enterprise analytics, as described in the
introduction. Another factor that makes such platforms attractive
is the ability to support analytics on unstructured data such as
text documents (including Web crawls), image and sensor data by
enabling execution of custom Map and Reduce
functions in a scalable manner. Recently, these engines have been
extended to support features necessary for enterprise adoption
(for example, Cloudera8). While
serious enterprise adoption is still in early stages compared to
mature parallel RDBMS systems, exploration using such platforms
is growing rapidly, aided by the availability of the open source
Hadoop14 ecosystem. Driven by the
goal of improving programmer productivity while still exploiting
the advantages noted here, there have been recent efforts to
develop engines that can take a SQL-like query, and automatically
compile it to a sequence of jobs on a MapReduce engine
(for example, Thusoo et al.32). The
emergence of analytic engines based on MapReduce is having
an impact on parallel DBMS products and research. For example,
some parallel DBMS vendors (for example, Aster
Data2) allow invocation of
MapReduce functions over data stored in the database as
part of a SQL query. The MapReduce function appears in the
query as a table that allows its results to be composed with
other SQL operators in the query. Many other DBMS vendors provide
utilities to move data between MapReduce-based engines and
their relational data engines. A primary use of such a bridge is
to ease the movement of structured data distilled from the data
analysis on the MapReduce platform into the SQL
system.

Near Real-Time BI. The competitive pressure of today’s
businesses has led to the increased need for near real-time BI.
The goal of near real-time BI (also called operational BI or
just-in-time BI) is to reduce the latency between when
operational data is acquired and when analysis over that data is
possible. Consider an airline that tracks its most profitable
customers. If a high-value customer has a lengthy delay for a
fight, alerting the ground staff proactively can help the airline
ensure that the customer is potentially rerouted. Such near
real-time decisions can increase customer loyalty and
revenue.

A class of systems that enables such real-time BI is Complex
Event Processing (CEP) engines (for example,
Streambase29). Businesses can specify
the patterns or temporal trends that they wish to detect over
streaming operational data (referred to as events), and take
appropriate actions when those patterns occur. The genesis of CEP
engines was in the financial domain where they were used for
applications such as algorithmic stock trading, which requires
detecting patterns over stock ticker data. However, they are now
being used in other domains as well to make decisions in real
time, for example, clickstream analysis or manufacturing process
monitoring (for example, over RFID sensor data).

CEP is different from traditional BI since operational data
does not need to be first loaded into a warehouse before it can
be analyzed (see Figure 4). Applications define
declarative queries that can contain operations over streaming
data such as filtering, windowing, aggregations, unions, and
joins. The arrival of events in the input stream(s) triggers
processing of the query. These are referred to as “standing” or
“continuous” queries since computation may be continuously
performed as long as events continue to arrive in the input
stream or the query is explicitly stopped. In general, there
could be multiple queries defined on the same stream; thus one of
the challenges for the CEP engine is to effectively share
computation across queries when possible. These engines also need
to handle situations where the streaming data is delayed,
missing, or out-of-order, which raise both semantic as well as
efficiency challenges.

There are several open technical problems in CEP; we touch
upon a few of them here. One important challenge is to handle
continuous queries that reference data in the database (for
example, the query references a table of customers stored in the
database) without affecting near real-time requirements. The
problem of optimizing query plans over streaming data has several
open challenges. In principle, the benefit of an improved
execution plan for the query is unlimited since the query
executes “forever.” This opens up the possibility of more
thorough optimization than is feasible in a traditional DBMS.
Moreover, the ability to observe execution of operators in the
execution plan over an extended period of time can be potentially
valuable in identifying suboptimal plans. Finally, the increasing
importance of real-time analytics implies that many traditional
data mining techniques may need to be revisited in the context of
streaming data. For example, algorithms that require multiple
passes over the data are no longer feasible for streaming
data.

Back to Top

Enterprise Search

BI tasks often require searching over different types of data
within the enterprise. For example, a salesperson who is
preparing for a meeting with a customer would like to know
relevant customer information before the meeting. This
information is today siloed into different sources: CRM
databases, email, documents, and spreadsheets, both in enterprise
servers as well as on the user’s desktop. Increasingly, a large
amount of valuable data is present in the form of text, for
example, product catalogs, customer emails, annotations by sales
representatives in databases, survey responses, blogs and
reviews. In such scenarios, the ability to retrieve and rank the
required information using the keyword search paradigm is
valuable for BI. Enterprise search focuses on supporting the
familiar keyword search paradigm over text repositories and
structured enterprise data. These engines typically exploit
structured data to enable faceted search. For example,
they might enable filtering and sorting over structured
attributes of documents in the search results such as authors,
last modification date, document type, companies (or other
entities of interest) referenced in documents. Today, a number of
vendors (for example, FAST Engine
Search11 and Google Search
Appliance12) provide enterprise
search capability.

A popular architecture for enterprise search engines is the
integrated model, shown in Figure 5. The
search engine crawls each data source and stores the data into a
central content index using an internal representation that is
suitable for fast querying. The configuration data controls what
objects to index (for example, a crawl query that returns
objects from a database) as well as what objects to return in
response to a user query (for example, a serve query to
run against the database when the query keywords match a crawled
object). Several technical challenges need to be addressed by
enterprise search engines. First, crawling relies on the
availability of appropriate adapters for each source.
Achieving a high degree of data freshness requires specialized
adapters that can efficiently identify and extract data changes
at the source. Second, ranking results across data sources is
non-trivial since there may be no easy way to compare relevance
across sources. Unlike ranking in Web search, links across
documents in an enterprise are much sparser and thus not as
reliable a signal. Similarly, query logs and click-through
information are typically not available at sufficient scale to be
useful for ranking. Finally, deploying enterprise search can
involve manually tuning the relevance, for example, by adjusting
the weight of each source.

Back to Top

Extract-Transform-Load Tools

The accuracy and timeliness of reporting, ad hoc queries, and
predictive analysis depends on being able to efficiently get
high-quality data into the data warehouse from operational
databases and external data sources. Extract-Transform-Load (ETL)
refers to a collection of tools that play a crucial role in
helping discover and correct data quality issues and efficiently
load large volumes of data into the warehouse.

Data quality. When data from one or more sources is
loaded into the warehouse, there may be errors (for example, a
data entry error may lead to a record with State = ‘California’
and Country = ‘Canada’), inconsistent representations for the
same value (for example, ‘CA’, ‘California’), and missing
information in the data. Therefore, tools that help detect data
quality issues and restore data integrity in the warehouse can
have a high payoff for BI. Data profiling tools enable
identification of data quality issues by detecting violations of
properties that are expected to hold in the data. For example,
consider a database of customer names and addresses. In a clean
database, we might expect that (Name, Address) combinations are
unique. Data profiling tools verify whether this
uniqueness property holds, and can quantify the degree to which
it is violated, for example, this might happen if Name or Address
information is missing. Data profiling tools can also
discover rules or properties that hold in a given
database. For example, consider an external data source that
needs to be imported into a data warehouse. It is important to
know which columns (or sets of columns) are keys (unique)
for the source. This can help in matching the incoming data
against existing data in the warehouse. For efficiency, these
tools often use techniques such as sampling when profiling large
databases.

Accurately extracting structure from a string can play
an important role in improving data quality in the warehouse. For
example, consider a shopping Web site that stores MP3 player
product data with attributes such as Manufacturer, Brand, Model,
Color, Storage Capacity and receives a data feed for a product as
text, for example, “Coby MP3 512MB MP-C756 – Blue.” Being
able to robustly parse the structured information present in the
text into the appropriate attributes in the data warehouse is
important, for example, for answering queries on the Web site.
Vendors have developed extensive sets of parsing rules for
important verticals such as products and addresses. The survey
article by Sarawagi28 discusses
techniques to the broader area of information extraction.

Another important technology that can help improve data
quality is de-duplication: identifying groups of
approximately duplicate entities (for example, customers). This
can be viewed as a graph clustering problem where each node is an
entity and an edge exists between two nodes if the degree of
similarity between two entities is sufficiently high. The
function that defines the degree of similarity between two
entities is typically based on string similarity functions such
as edit distance (for example, ‘Robert’ and ‘Robet’ have an edit
distance of as well as domain-specific rules (for example, ‘Bob’
and ‘Robert’ are synonymous). Thus, the ability to efficiently
perform such approximate string matching across many pairs of
entities (also known as fuzzy matching) is important for
de-duplication. Most major vendors support fuzzy matching and
de-duplication as part of their ETL suite of tools. An overview
of tools for merging data from different sources can be found in
Bernstein.3

Data load and refresh. Data load and refresh utilities
are responsible for moving data from operational databases and
external sources into the data warehouse quickly and with as
little performance impact as possible at both ends. There are two
major challenges. First, there is a need to efficiently
capture data at the sources, that is, identify and collect
data to be moved to the data warehouse. Triggers are
general-purpose constructs supported by SQL that allow rows
modified by an insert/update SQL statement to be identified.
However, triggers are a relatively heavyweight mechanism and can
impose non-trivial overheads on the operational database running
OLTP queries. A more efficient way of capturing changed data is
to sniff the transaction log of the database. The transaction
log
is used by the database system to record all changes so
that the system can recover in case of a crash. Some utilities
allow pushing filters when processing transaction log
records, so that only relevant changed data is captured; for
example, only changed data pertaining to a particular department
within the organization.

The second aspect relates to techniques for efficiently
moving captured data into the warehouse. Over the years,
database engines have developed specialized, performance
optimized APIs for bulk-loading data rather than using standard
SQL. Partitioning the data at the warehouse helps minimize
disruption of queries at the data warehouse server. The data is
loaded into a partition, which is then switched in using a
metadata operation only. This way, queries referencing that table
are blocked only for a very short duration required for the
meta-data operation rather than during the entire load time.
Finally, load utilities also typically checkpoint the operation
so that in case of a failure the entire work does not need to be
redone. Using the techniques discussed above for capturing
changed data and efficient loading, these days utilities are able
to approach refresh rates in a few seconds (for example, Oracle
GoldenGate22). Thus, it is
potentially possible to even serve some near real-time BI
scenarios, as discussed earlier.

Back to Top

Other BI Technology

Here, we discuss two areas we think are becoming increasingly
important and where research plays a key role.

Data Mining and Text Analytics. Data mining enables
in-depth analysis of data including the ability to build
predictive models. The set of algorithms offered by data mining
go well beyond what is offered as aggregate functions in
relational DBMSs and in OLAP servers. Such analysis includes
decision trees, market basket analysis, linear and logistic
regression, neutral networks and more (see
survey6). Traditionally, data mining
technology has been packaged separately by statistical software
companies, for example, SAS,26 and
SPSS.27 The approach is to select a
subset of data from the data warehouse, perform sophisticated
data analysis on the selected subset of data to identify key
statistical characteristics, and to then build predictive models.
Finally, these predictive models are deployed in the operational
database. For example, once a robust model to offer a room
upgrade to a customer has been identified, the model (such as a
decision tree) must be integrated back in the operational
database to be actionable. This approach leads to several
challenges: data movement from warehouse to the data mining
engine, and potential performance and scalability issues at the
mining engine (or implied limitations on the amount of data used
to build a model). To be practical, such models need to be
efficient to apply when new data arrives. Increasingly, the trend
is toward “in-database analytics,” that is, integrating the data
mining functionality in the backend data-warehouse architecture
so that these limitations may be overcome (for example, Netz et
al.20 and Oracle Data
Mining21).

Text analytics. Consider a company making portable
music players that conducts a survey of its products. While many
survey questions are structured (for example, demographic
information), other open-ended survey questions (for example,
“Enter other comments here”) are often free text. Based on such
survey responses, the company would like to answer questions such
as: Which products are referenced in the survey responses? What
topics about the product are people mentioning? In these
scenarios, the challenge is to reduce the human cost of having to
read through large amounts of text data such as surveys, Web
documents, blogs, and social media sites in order to extract
structured information necessary to answer these queries. This is
the key value of text analytic engines. Today’s text analysis
engines (for example, FAST11 and
SAS26) primarily extract structured
data that can be broadly categorized as: Named entities
are references to known objects such as locations, people,
products, and organizations. Concepts/topics are terms in
the documents that are frequently referenced in a collection of
documents. For example, in the above scenario of portable music
players, terms such as “battery life,”https://r.search.aol.com/”appearance,” and
“accessories” may be important concepts/topics that appear in the
survey. Such information can potentially be used as a basis for
categorizing the results of the survey. Sentiment analysis
produces labels such as “positive,”https://r.search.aol.com/”neutral,” or “negative” with
each text document (or part of a document such as a sentence).
This analysis can help answer questions such as which product
received the most negative feedback.

Cloud Data Services. Managing enterprise BI today
requires handling tasks such as hardware provisioning,
availability, and security patching. Cloud virtualization
technology (for example, Amazon EC21)
allows a server to be hosted in the cloud in a virtual machine,
and enables server consolidation through better utilization of
hardware resources. Hosted servers also offer the promise of
reduced cost by offloading manageability tasks, and leveraging
the pay-as-you-go pricing model to only pay for services that are
actually used. The success of hardware virtualization in the
cloud has prompted database vendors to virtualize data services
so as to further improve resource utilization and reduce cost.
These data services initially started as simple key-value stores
but have now begun to support the functionality of a single node
relational database as a hosted service (for example, Microsoft
SQL Azure18). While the primary
initial users of such cloud database services are relatively
simple departmental applications (OLTP), the paradigm is being
extended to BI as well (for example,
Pentaho25).

The need for the full range of BI services over the data
collected by these applications raises new challenges for cloud
database services. First, the performance and scale requirements
of large reporting or ad hoc queries will require database
service providers to support a massively parallel processing
system (parallel DBMS and/or MapReduce-based engine) in the
cloud, Second, these services are multi-tenant, and complex SQL
queries can be resource intensive. Thus, the ability to provide
performance Service Level Agreements (SLAs) to tenants and
judiciously allocate system resources across tenant queries
becomes important. Third, many of the technical challenges of
traditional “in-house” BI such as security and fine grained
access control become amplified in the context of cloud data
services. For example, techniques for processing queries on
encrypted data become more important in public clouds. For these
reasons, an intermediate step in adoption of BI technologies may
be in private clouds, which hold promise similar to public clouds
but with more control over aspects such as security.

Back to Top

Conclusion

The landscape of BI in research and industry is vibrant today.
Data acquisition is becoming easier and large data warehouses
with 10s to 100s of tera-bytes or more of relational data are
becoming common. Text data is also being exploited as a valuable
source for BI. Changes in the hardware technology such as
decreasing cost of main memory are impacting how the backend of
large data-warehouses are architected. Moreover, as cloud data
services take root, more changes in the BI backend architecture
are expected. Finally, there is increasing demand to deliver
interactive BI experiences on mobile devices for today’s
knowledge workers. There are ample opportunities to enable novel,
rich, and interactive BI applications on the next generation of
mobile devices. Thus, business intelligence software has many
exciting technical challenges and opportunities still ahead that
will continue to reshape its landscape.

Back to Top

References

1. Amazon EC2;
http://aws.amazon.com

2. Aster Data;
http://www.asterdata.com.

3. Bernstein, P. and Haas, L. Information
integration in the enterprise. Commun ACM 51, 9 (Sept.
2008).

4. Chaudhuri, S. An overview of query
optimization in relational systems. ACM PODS 1998.

5. Chaudhuri, S. and Dayal, U. An overview of
data warehousing and OLAP technology. SIGMOD Record 26, 1
(1997).

6. Chaudhuri, S., Dayal, U. and Ganti, V.
Database technology for decision support systems. IEEE
Computer 34
, 12 (2001).

7. Chaudhuri, S. and Narasayya, V.
Self-tuning database systems: a decade of progress. In
Proceedings of VLDB 2007.

8. Cloudera Enterprises;
http://www.cloudera.com

9. Dean, J. and Ghemawat, S. MapReduce:
Simplified data processing on large clusters. In Proceedings
of OSDI 2004
.

10. DeWitt D.J. and Gray J. Parallel
database systems: The future of high-performance database systems
Commun. ACM 35, 6 (June 1992).

11. FAST Enterprise Search;
http://www.fastsearch.com

12. Google Search Appliance;
http://www.google.com/enterprise/gsa

13. Graefe, G. Query evaluation techniques
for large databases. ACM Computing Surveys 25, 2 (June
1993).

14. Hadoop;
http://hadoop.apache.org

15. IBM Cognos;
http://www.ibm.com

16. Isard et al. Dryad: Distributed
data-parallel programs from sequential building blocks. In
Proceedings of Eurosys 2001.

17. Microsoft SQL Server Analysis Services;
http://www.microsoft.com

18. Microsoft SQL Azure;
http://www.microsoft.com

19. Netezza;
http://www.netezza.com

20. Netz, A., Chaudhuri, S., Fayyad, U. and
Bernhardt, J. Integrating data mining with SQL databases. OLE
DB for Data Mining
, 2001.

21. Oracle Data Mining;
http://www.oracle.com

22. Oracle GoldenGate;
http://www.oracle.com

23. Oracle Hyperion;
http://www.oracle.com

24. QlikView;
http://www.qlikview.com

25. Pentaho;
http://www.pentaho.com

26. SAS: Business Analytics and Business
Intelligence Software;
http://www.sas.com

27. SPSS: Data Mining, Statistical Analysis,
Predictive Analytics, Decision Support Systems;
http://www.spss.com

28. Sarawagi, S. Information extraction.
Foundations and Trends in Databases 1, 3 (2008),
261–377

29. Streambase;
http://www.streambase.com

30. Sybase IQ;
http://www.sybase.com

31. Teradata;
http://www.teradata.com

32. Thusoo, A. et al. Hive—A
warehousing solution over a MapReduce framework. VLDB 2009

Back to Top

Authors

Surajit Chaudhuri
(surajitc@microsoft.com)
is a principal researcher at Microsoft Research, Redmond, WA.

Umeshwar Dayal
(umeshwar.dayal@hp.com)
is an HP Fellow in the Intelligent Enterprise Technology Labs at
Hewlett-Packard Lab, Palo Alto, CA.

Vivek Narasayya
(viveknar@microsoft.com)
is a principal researcher at Microsoft Research, Redmond, WA.

Back to Top

Figures

F1Figure 1. Typical business intelligence
architecture.

F2Figure 2. Multidimensional data.

F3Figure 3. Snowflake schema.

F4Figure 4. Complex event processing server
architecture.

F5Figure 5. Enterprise search architecture (integrated
model).

Back to top


©2011
ACM  0001-0782/11/0800  $10.00

Permission to make digital or hard copies of part or all of
this work for personal or classroom use is granted without fee
provided that copies are not made or distributed for profit or
commercial advantage and that copies bear this notice and full
citation on the first page. Copyright for components of this work
owned by others than ACM must be honored. Abstracting with credit
is permitted. To copy otherwise, to republish, to post on
servers, or to redistribute to lists, requires prior specific
permission and/or fee. Request permission to publish from
permissions@acm.org or
fax (212) 869-0481.

The Digital Library is published by the Association
for Computing Machinery. Copyright © 2011
ACM, Inc.


Comments


Anonymous

I was surprised not seeing Datameer mentioned as a new up and coming big data BI player. Please explain why? Thank you in advance


Displaying 1 comment

Source Article