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Volume 4.3M Shalby Ltd Aug 16 Chelsea Roh 2 Views All Files Transfer Funds … I NTRODUCTION Information Technology has revolutionized almost every profession we know of. Financial Investors around the world are now capable of viewing and learning about changes in any country, organization, or topic while seated in their offices. These investors rely extensively on the information available on the Web and in particular the live up-to-date information to make informed decisions for their investments. Nowadays, the Internet is one of the main sources of dynamic real-time investment- related information. The Internet hosts numerous sources of such information that anyone can access and use for their benefit. This information is provided in the form of HTML WebPages, XML documents, web services, remote data stores, etc. It can also ne be downloaded and stored in relational databases (DB) or other specialized DBs like temporal [5], Active [22] and time series [26] databases. It is scattered and distributed across hundreds, if not thousands, of sites and most of it is updated very frequently. In addition, the events and changes in investment-related fields occur frequently and in non-deterministic patterns. Therefore, the investor’s ability to follow and analyze this information becomes limited and he/she is forced to ignore many pieces of essential information when making on-the-spot decisions. For example, if an investor is managing a stocks portfolio containing stocks from multiple companies in various countries, it is essential to follow all events and changes occurring for any and all of them before making decisions to buy or sell any of these stocks or rebalance the portfolio. Having to go through company financial records, stock prices, volumes being sold, demand values, currency exchange rates in addition to other information related to the countries and organizations current conditions requires long hours of monitoring and analysis. However, investors usually do not enjoy the luxury of time. In an ideal situation, decisions must be made within minutes of perceived events. A number of tools were developed to monitor and analyze financial information [1]. These tools use different types of monitoring models. In this paper we provide a classification and discussion of different web financial monitoring models such as restricted, unrestricted, calculated, temporal and group monitoring models. We identify temporal monitoring as the most technically challenging as it must take into account data changes with respect to time and occurring events. An example of this monitoring model is to monitor a financial value (e.g. stock price, oil prices, or some other dynamically changing value) to find a certain increase or decrease by certain amount within a predefined period of time. Such monitoring requires continuous retrieval and storage of the data relevant to the required value and evaluating it to see if the condition is met. As a result a huge amount of storage may be needed and processing may also require a long time. In this paper we also develop an efficient storage algorithm for temporal financial monitoring. This algorithm reduces the amount of storage needed for this type of monitoring and it provides fast financial monitoring. Compared to the traditional temporal databases, which store all temporal data observed then run specialized temporal queries to obtain the desired result, our approach applies the temporal conditions as it observes the incoming data and only stores the data sets relevant to the required outcome. As a result, the algorithm reduces storage requirements as it does not need to keep all observed information and the online monitoring of the data as it is retrieved will speed up the processing to match the required condition. For example, if we need to find if a certain stock price has increased by 5% within one hour during the coming week, a temporal database will store all the weeks data and run the condition to find possible matches periodically. In our approach we examine incoming data and only keep the information that could lead to satisfying the condition. Significantly reducing the amount of storage needed. Now consider the situation where a service provides this type of monitoring to thousands of investors. In this case we have to consider the problem as one requiring high performance computing capabilities in terms of processing power and storage size and access speeds. A service of this type will need to keep track of hundreds of financial data streams, store a huge amount of data for each one and deliver the results of the conditions matches in real-time. All this require the design and use of efficient techniques in monitoring, storing and analyzing the data to find the required results. In this regard and due to the increased size and requirements of financial applications some researchers started considering the Cloud as a possible platform for such applications as they can offer the needed HPC facilities and can tolerate the dynamic changes in the load on such applications. For example, in [8] the authors propose a real-time Cloud-based financial system. In another approach intelligent agents were proposed to offer financial market risk monitoring [29]. Similarly the intelligence cloud [10] provides a widely distributed approach to monitor financial entities, detect problems and fraud attempts, and alert the entities in real-time. Although we only approach financial information in this paper as our target application, the same arguments apply to many different domains where large amounts of live data is generated and need to be monitored for specific conditions. For example GIS systems can be targeted when we need to monitor traffic patterns across multiple areas and be informed of traffic conditions need to be monitored for certain events like accidents or traffic jams. Another possible area is in the medical field, where patient monitoring systems generate a lot of data about the patients being monitored and certain events need to be observed and responded to quickly [22]. In addition, monitoring large distributed networks and systems is another application where we need to monitor incoming observations about the status and conditions of the different components in the system and respond to the changes in real-time [20]. In the rest of this paper Section II is an overview of financial web information. A classification and discussion of different financial web information monitoring models is in Section III. Section IV provides other’s work related to temporal data and monitoring and Section V discusses the storage challenge of temporal financial monitoring models and provides an efficient algorithm to solve it. The experimental evaluation of the algorithm is in Section VI and Section VII concludes the paper. II. F INANCIAL W EB I NFORMATION There are different types of financial information sources available over the Internet. These sources provide financial information through dynamic HTML documents [11], XML documents [7], or web services [2]. These sources provide different financial information that we can call Internet variables. Each Internet variable is dynamic and provides a current number for a specific financial value. A middleware called MidWire was proposed to efficiently reuse the available web information [16]. This approach allows for a generic model to access such data from any open source on the Web compared to currently available services that are usually restricted to one or a few specific sources. This Section discusses the three main ways for defining Internet variables depending on their sources. Web services provide a structured and simplified way to obtain services or specific information from the Internet. A Web service is defined by the World Wide Web Consortium (W3C) as “a software system designed to support interoperable Machine to Machine interaction over a network.” Web services provide web APIs that can be accessed over a network, such as the Internet, and executed on a remote system hosting the requested services. These remote systems support different services incl
uding providing information about different aspects or products. For example, different stock markets may host web services to provide current stock prices. Banks can use web services to provide information about interest rates on loans or information about foreign currency exchange rates. The W3C Web service usually consists of clients and servers that communicate using XML messages following the SOAP standard. If desired information is needed by a monitor application, then the user can easily define an Internet variable and link it with the web service that provides this information. The main problem with web services is that a limited amount of information available over the Internet is provided using Web Services. Another source of information on the Internet is XML documents. These offer a structured format for data in text-based documents that user programs can scan through and locate the required data easily. The data items in an XML document are associated with special tags that define the data semantics. This allows the monitor to retrieve the required information based on their defined tags. For example, in an XML document the stock price for a company will have a unique tag identifying the company and the type of data (price). Therefore, the user can write a program that locates the tag and use the data associated with it. However, like web services, XML does not offer a large source of information as a small percentage of the information available on the Internet is written in XML. Most of the Internet information is delivered to users in HTML documents. Unlike XML, HTML documents do not have any semantics to identify the content. Obtaining specific data from a dynamic HTML document for reuse in other applications is a complex task. It is very difficult to identify the required parts of the data and dynamically use them in other applications. We recently developed a simple and efficient approach for retrieving live HTML-based Internet information [17]. This approach is used to define the notification variables that will have their data updated from the Internet. The approach finds fixed titles or headers that appear in the HTML documents directly or semi- directly before the needed data. These fixed headers are used as references (markers) to identify the position of the required data. III. F INANCIAL I NFORMATION M ONITORING M ODELS There are different models for monitoring financial information. Some of these models are already deployed and implemented by some web applications. In addition, there are a number of advanced models that have the potential to be utilized for different applications in the near future. This section discuses these models. In this type of monitoring, monitoring fields, information, and conditions are restricted. The users have a limited set of predefined information sets and conditions that they can use to define their monitoring interests. It is used by some financial service providers over the Internet. One example of this type of monitoring is offered by Yahoo Stocks Watch Alert [30]. It provides users with options to set their notifications for stocks information. These options are based on changes in stock prices either in value or percentage. For example, the user can elect to receive a notification whenever the Wal-Mart stock price increases above $50.00 or when the price drops below $44.00. Figure 1 shows the Yahoo Stock Alert setup screen, the user can receive notifications as an email message, an instant message or a text message over the mobile phone. This type of monitoring is simple and can be easily used by regular users; however, it only provides basic monitoring conditions. Thus users cannot define advanced or complex conditions that may involve more than one value, multiple, or time-based changes. In addition, the information used for the alerts are limited to those owned or managed by the service provider only. Therefore, updates and changes in other websites or companies may not be included. For example, an investor can set a notification condition in Yahoo Finance such that whenever the Wal-Mart stock price drops to a specific value in US dollars a notification is sent. However, the investor can not define advanced notification criteria such as “when the trading volume reaches a specific quantity AND when the Wal-Mart stock price reaches a specific price in Euros;” although, both the trading volume and the USD to Euro exchange rates are available over the Internet. As Yahoo Finance does not support currency exchange information and cannot handle a combination of conditions, it will not be able to satisfy the investor’s requirements. This type of monitoring is still in the research stage, but shows potential of becoming very popular. It allows users to define monitoring conditions based on any values publicly available over the Web through web services or dynamic HTML documents and build alert conditions using these values. For example the user can define the criteria to monitor the trading volume of ALDAR Properties that is shown on the ADX website (see Figure 2). The user can define to get an alert whenever that number reaches a certain value [19][18][12]. In this type of service, the user defines the required value by identifying a fixed text item that appears before the required value so that it is possible to find it and retrieve the value [17]. The retrieved values are evaluated based on the user conditions and an alert is sent when the conditions are met. The approach solves the lack of identifying tags issue in HTML documents by using visual markers. A marker is a fixed text located within a known distance from the required piece of data and used to parse the document. This is necessary since we are not dealing with a single information provider that could have internal representations of the values and access them directly. Being in an HTML format makes it impossible to identify changing variables within the page directly. Therefore, it is accessed based on the overall format of the page and the fixed titles used. The proposed approach is developed as a Java class. Multiple objects can be created from this class for different Internet HTML documents that contain some of the required information [17]. If the information needed is available by web services, then users can use the corresponding web service and integrate it with the notification service. This type of monitoring is similar to the unrestricted monitoring. However, the needed information is obtained from multiple sources into multiple Internet variables. In addition, new variables can be calculated from the retrieved Internet variables. In this type of service the user defines the Internet variables and their sources then describes the calculated variables as combinations (usually mathematical) of the Internet variables. The Internet variables and/or the calculated variables can be to define the alert conditions. To illustrate this alert type, consider an example of an investor (using the Euro as a trading currency) and needs to know when the price of EMAAR stock listed in Dubai Financial Market (DFM) in Dirhams rises above 2 Euros. Two types of information are needed here, the stock price in Dirhams offered by DFM website (see Figure 3) and the Dirhams to Euro exchange rate offered on a currency exchange website (see Figure 4). Both values are dynamic and finding the price requires the investor to continuously watch the two sites and convert the currency on the listed price until the desired value is reached. However, using the monitoring service the investor will automate the process by defining two Internet variables for the current stock price in Dirhams, EmaarAED , and the current exchange rate from Dirhams to Euro, AEDEUR . The user then uses these two Internet variables to derive a calculated variable, EmaarEUR , for the current stock price in Euro ( EmaarEUR = EmaarAED * AEDEUR ). After that, the investor specifies the condition at which an alert message will be sent as soon as the price reaches the desired value in Euros. This type of monitoring service provides flexibility for the users to defi
ne advanced conditions. This is very important for stock investors [1] for example as hundreds of sites offer live financial information updated by the second. In addition, financial decisions also require monitoring other affecting variables such as the companies’ performance, the political and social asp ects affecting them and many other global events that may pose a significant impact on the financial markets. Investors rely on the collective knowledge to make their decisions, yet, in a fast paced world, events and changes occur in a fast, dynamic and non- deterministic manner. Therefore, the investors ’ ability to follow and analyze the information becomes limited and he/she is forced to ignore many pieces of essential information when making on- the-spot decisions. Making such decisions without the proper tools will require significant efforts and may also need more time than practically available to make them. This type of alert service provides a useful tool that helps stock investors to take such fast decisions by allowing them to combine several values and respond to the notifications received when the conditions are met. In this information monitoring model time is part of the defined monitoring conditions. That is to satisfy a monitoring condition of this type, the service must keep track of some values and their changes over a specified period of time. This requires the user to identify the relevant Internet variables, the time-based change factors, and the amount of time required for that change. An example of this monitoring is an alert that should be sent when the Microsoft stock price drops by $4 within one hour. In this case the service needs to keep track of all changes of the Microsoft stock price over time and compare the values until a difference of $4 is perceived within one hour. generally, this requires keeping all price values over time and when needed, a query is issued to search through the entries to find a change that matches the condition. Related Internet information can be scattered over multiple web pages or available on a single web page. It may be provided by multiple web services or by a single web service. Example of this type of information is all the stock information listed in NASDAQ market in USA. The user can define a group alert such that he/she will be notified when the price of any stock listed in NASDAQ increases by 30% from the opening price. This alert also deals with multiple related pieces of stock information that could be scattered over multiple web pages or web services on the Internet. In this case the service does not have one or a few defined variables to monitor. Instead, it will need to keep track of all values available that are relevant to what the conditions the user defined. Optimizations may be done to the process by compressing the data for example or using a cutoff period (for example one day or one week) to limit the size of stored data. However, this may limit the user’s options when defining the required temporal conditions. Several other types of monitoring conditions can be derived from combining some or all of the types discussed above. In this case more complex methods may be needed to provide the user with the right tools to define the alerts conditions. For example, it is possible to combine group alerts with temporal alerts to watch for time-based changes across multiple values either from the same source or multiple sources. For example a user may want to know whenever any stock listed in NASDAQ drops by 20% within three hours. This type of monitoring is both temporal and group monitoring. It requires the user to define the monitored NASDAC stocks information in addition to the timing conditions for the monitor. Calculated monitoring may also be combined with temporal and/or group monitoring to define advanced conditions regarding the available information that is normally not offered directly by the information providers. In this case, the user may select several pieces of information from multiple sources, define calculated variables to identify some form of dependency between the variables, then issue specific alert conditions either as open conditions or time-based conditions. IV. T IME R ELEVANT D ATA MA NAGEMENT Our main contribution in this paper in addition to classifying the monitoring types is to develop an efficient storage model for temporal information monitoring. Therefore, we concentrate here on research efforts that could be useful to address this problem. As we explored other research approaches, we came across three interesting models, namely: Active DB, Temporal DB and Time Series DB; that carry some similarities to the main concepts of our work. However, they do not offer a specific solution that can be directly compared to our work. The closest techniques suitable for temporal monitoring we found are those using the Time Series DB (TSDB) with compressed data [14] and [20], yet they still cannot reduce the size of the data significantly as they still record data over extended period of time, while in our approach we incorporate the time condition from the beginning thus data is only stored if it satisfies to this time condition. Temporal databases (DB) offer one model that takes time as the main factor when storing and querying data [5]. It was devised when the issue of adding time information as key fields in relational DBs resulted in high levels of redundancies in stored data. Therefore, temporal DBs allow for storing the time relevant information in a more efficient way. There are two types of time stamps that are used: Valid Time which indicates the time the entry is true and Transaction Time which indicates the time the fact was entered into the DB. One example application domain that benefits from using temporal DBs is the geographical information systems (GIS). GIS system designers use temporal DBs to a temporal GIS capability to trace geographical change and understand geographical processes [9] in addition to being able to record historical changes and represent them in an accessible model [24]. Another example is using it to manage temporal financial data. Here the use of extensible database management systems (DBMS) is devised to allow for incorporation time, rule and object constraints affecting financial transactions data. Therefore, it becomes easier to query the information based on these particular variants [3]. An active DB strives to offer a model that allows the DB to respond automatically to events occurring inside and outside the DB. It is considered a rule-based system where rules governing certain events and conditions and resulting in specific actions are defined in the DBMS using the ECA-Rule (Event, Condition, Action). Different types of applications may benefit from this approach such as transaction models; coordination of distributed computing; monitoring and reacting to financial data changes in portfolio management systems; and sensing/monitoring applications used in battle fields, patient monitoring and air traffic control [22]. Several areas where active DB can be used have been explored in the past decade. We list here a few examples that relate in some way to the work introduced in this paper. FFML [6] is a rule-based policy modeling language and architecture that facilitate the expression and implementation of proactive fraud controls for financial service platforms through active monitoring and compliance functions. Active CEN [28] introduces an active rule support to CEN (Complex Event Endowment) to help manage the queries and the reactions to them in high volume streams. It uses a stream-oriented transactional model for scheduling streams executions efficiently. Percolator [23] is a system for incrementally processing updates to large data sets. This allows the system to increase the efficiency of applications that require manipulating and updating large data sets such as the Google indexing system. In [15] iPHR (intelligent Personal Health Record) is transformed into an Active iPHR by introducing triggers and monitoring. Time Series DB (TSDB) is a software system that facilitates the storage, in
dexing and querying of large time series data (arrays of data indexed by timestamps). For example we could have a series that represents a stock price over time (aka price curve). TSDB allows users to create, enumerate, update and destroy time series and also allows them to organize, filter, merge and query them in an easy way [26]. Currently there are some implementations of TSDB such as OpenTSDB [21], kairosdb [13], TempoDB [25] and T imeScape’s Tick TSDB [ 27]. Several research groups also worked in this direction recently with the emergence of issues related to bigdata and large time related data series. Several approach the search methods, analysis, matching models and datamining approaches for large time series data. In [14] a model for multiresolution time DBMS is designed to compactly store a time series and consistently manage its temporal dimension. In addition, [4] introduce a compressed DB for time series called tsdb that consolidates network monitoring time series data in real-time to limit disk space usage. In [20] the authors introduce an algorithm for periodic pattern mining in TSDBs to detect all types of periodic patterns at the same time. V. E FFICIENT T EMPORAL F INANCIAL M ONITORING In the temporal financial monitoring model we propose, users can specify to monitor any publicly available Web values (user defined Internet variables) over a period of time. The users define the period during which the values of these Internet variables need to be monitored for specific changes. There are two condition types of temporal monitoring. The first is the incremental condition monitoring, where users are interested to know whenever the value of an Internet variable has increased by a specified amount within a defined period of time. For example, a user needs to know when the X stock has increased by Y% within Z hours. The second is the decremental condition monitoring, where users are interested to know whenever the value of a monitored element has decreased by a specific amount within a defined period of time. In both cases, some temporal provisions need to be used to find, store, compare and produce the required notification. A basic approach to implement temporal web alerts is to store all values of the monitored elements over the period defined by the user and check the alert condition with each update. If the element value changes every k seconds and the alert condition period is p seconds, than we need to store p/k values. This will require a large storage space specially if k is small and p is very large. We call this approach a periodic-storage approach in which each updated value of the monitored element will be stored, Temporal DBs and TSDBs are capable of handling such approach efficiently. However, as the number of requests increases and time periods extend over long periods, the storage requirements increase significantly causing problems if storage is limited. Another approach to use for storing the value changes over the condition period is to store new values and their time if and only if there is a new change in the value. This approach will store a tuple (value, time) with two numbers for each change: the value and the time of recording it. We call this approach a time- stamped-storage approach. In this approach, if there is a change for every k seconds then space for 2p/k elements is needed, thus doubling the space required compared to the periodic-storage approach. However, if there is, on average, a change every 2k or more seconds then the storage space required is equal to or less than p/k . With less frequent changes in the values the required storage space becomes much smaller than that needed for the periodic-storage approach. However, we still need a significantly large storage space to satisfy the issued conditions. To overcome the storage problem, we develop an efficient approach for supporting temporal web alerts by storing the minimum number of values required to test the user-defined temporal condition. This approach is similar to the time-stamped- storage approach; however, by adding some intelligent storage criteria it needs less storage space. We call this approach efficient- time-stamped-storage approach. This approach relies on the fact that the value of a monitored element may increase and decrease several times during the monitoring period. Based on the conditions issued by the users (incremental or decremental), change in the opposite direction of the condition may not be useful for evaluating the condition. Therefore, it is possible to ignore some values that will not affect the decision for the required condition. For an incremental condition, we need to store values as they increase over time. Yet, as soon as the current value becomes smaller than some stored values, there will be no need to keep any stored values that are larger than the current value. For example, if a user needs to know if the Wal-Mart stock price increases $5 within one hour (starting at 10:15). The starting price of $43 is stored then we keep monitoring. Assume at 10:18 the price is $46, then there is no need to keep any stored values larger than or equal to $46 (e.g. $47 recorded at 10:17). However, the current value must be stored. The stored current value will cover for the higher values recorded earlier as in Figure 5; all values above the dotted line between points a and b will not need to be stored for future comparisons as they will not affect the final result. Formally, with the incremental condition we have two possible changes for the tuples (Time i , Value i ) and (Time j , Value j ), where tuple i is the latest stored value and tuple j is the current value. If Time j > Time i , and Value j <= Value i , then we store (Time j , Value j ) and we remove the previous value (Time i , Value i ) and all earlier tuples k where Value k >= Value j . Otherwize, if Value j > Value i , then we add the new value to the stored list. The current tuple j must be stored since after a while it may become the first value in the list to use for comparison. This happens because the storage is circular based on the defined period T so any tuple k will be removed when Time j – Time k > T . For example, there is no need to keep any values that occurred more than one hour earlier if the temporal alert condition time is one hour. Based on the technique described here, the stored values will be incrementally sorted with time since no larger values will be kept in the list if the current value is less. Only older and lower values will be kept and only for the condition time period. Applying the same principle, we can manage the time/value tuples for the decremental condition. Here we will keep values as they decrease and will not need to store values when they become smaller than the current value. For example, we have a decremental condition and two changes: (Time i , Value i ) and (Time j , Value j ). If Time j > Time i and Value j >= Value i , then we store the new value (Time j , Value j ) and we remove the previous value (Time i , Value i ) and all the earlier tuples k where Value k < Value j . Otherwise, we just store the current tuple j . To design an algorithm for this technique, we used a circular list. The time stamped values are added to the circular list for new values and old expired values (when the time has passed the condition time) are removed from the beginning of the circular list. Table 1 provides a list of the symbols used in the algorithm and their meanings. The developed algorithm is in Figure 6. This algorithm is for the incremental condition only. However, the algorithm for the decremental condition is similar with changes in the comparison conditions. This algorithm is used every time a new value is obtained from the web. The algorithm will update the circular list such that only values less than the current values are kept and all other values are removed. The new value will be added to the end of the list and the expired values at the beginning of the list will be removed. In addition to updating the circular list, the algorithm will test the temporal condition and return the test result. Therefore, it will be easy to quickly determine of the condition is met as soon as any change is recorded. The approach allows us to limit the space neede to store the values needed for comparison within the defined time period rather than having to continuously store all values for an extended period of time. For example, if the set temporal condition is for a period of one hour and it should run over a full working day (e.g. wight hours) then with continuous storage we need to keep information for the whole eight hours, while in our approach we only need to keep values over one hour. In addition within this hour, if values become irrelevant to the comparison as we described earlier, then we do not keep them. Thus further reducing the overall storage needs. Applying this to multiple requests for multiple temporal monitoring conditions, we can significantly reduce the storage space needed while providing real-time results to for the users. VI. T HE E XPERIMENTAL E VALUATION To show the advantage of our temporal monitoring approach we performed a set of experiments to monitor the price of the EMAAR stock listed in Dubai Financial Market (DFM) using the three temporal monitoring approaches discussed in the Section IV. In the experiments the temporal web alert used an incremental condition. The condition requires an alert to be sent whenever EMAAR stock price increases by AED 0.5 within one hour. For all the approaches tested a price value is received every 10 seconds. In the first approach, we stored all received data and checked the condition with each update. In the second approach we used a time-stamped tuple to store values/time recorded and we ignored the values that did not change from the previously recorded value. In the third approach we used the circular list and value checks in our approach to eliminate all irrelevant values. The monitoring continues until the condition is met. Table 2 shows the average storage space required for each of the three approaches. The storage space unit used represents the space needed to store a single element (e.g. the value in the periodic storage and the time and value elements in the other two approaches). As we can see the efficient-time-stamped approach needs less storage compared to both the periodic-storage approach and the time-stamped-storage approach. We were not able to compare our approach to Temporal, Active and Time Series DBs since they all represent data that is currently stored in the DB in a manner accessible with time indexes or defined rules. As a result, to make the comparison we need to first get and store the monitored values in the DB then apply the conditions. This is relatively counterproductive as we include an extra step in the process and we also cause delays until we can get the result to the user. In addition, with multiple requests, it becomes hard to organize the data in an efficient manner and make multiple condition checks quickly. However, our approach may benefit greatly from the mechanisms and models offered by these DBs to further improve the performance and optimize the operations especially for handling multiple users and multiple conditions from multiple sources. We plan to investigate the issue further in our future work on this approach. VII. C ONCLUSION In this paper we aimed to satisfy two objectives. First, we provided a classification of different web financial monitoring models. These monitoring models are Restricted, Unrestricted, Calculated, Temporal, Group, and advanced monitoring models. This classification allowed us to identify the potential of the approaches and how further improve Web information monitoring to satisfy complex users’ requirements. Then we focused on one approach, the temporal monitoring, as it represents a crucial service for investors that could greatly improve their decision making process. We discussed the storage challenge for supporting temporal web financial information monitoring since satisfying temporal conditions requires keeping all information related to it over the full duration of time requested. A direct method to store all values results in the use of huge storage space. Therefore, we introduced and evaluated an efficient algorithm for reducing the storage requirements. In this approach only the values relevant to testing the monitoring condition are stored rather than storing all values. This is possible since fluctuations in the opposite direction of the test condition will result in values that are redundant and not necessary to consider while trying to satisfy the monitoring condition. In addition, the technique also uses a circular storage such that all values outside the test time period being considered are removed. This technique significantly reduces the required storage to support temporal web alerts. This technique can also be used in many other application domains where dynamic fluctuating data need to be monitored for certain conditions. One example is in patient monitoring using wearable sensors. In this case it may be of interest for example for a doctor to monitor the increase in blood pressure over a specific period of time. GIS, distributed systems and networks monitoring, logistics systems monitoring are also a few suitable examples. We intend to generalize the approach and offer a generic tool to support temporal monitoring for any type of application where the needed data is publicly available on the ... FMCG VR & mixed reality Mutual Fund Screeners Charter Communications inc. 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